Trabajo de fin de máster
Nuestro objetivo es detectar a través de diferentes técnias de machine learning, aquellas páginas web que tratan de llevar a cabo la técnica de engaño “phishing”.
Cargamos la librerías que usaremos a lo largo de todo el trabajo.
Cargamos los datos brutos.
phishing_bruto <-
read.csv("dataset_full.csv")
Comenzaremos observando la estructura de nuestros datos y obteniendo una visión general de nuestras variables, donde también nos centraremos en como se distribuyen los niveles de nuestra objetivo (phishing).
glimpse(phishing_bruto)
Rows: 88,647
Columns: 112
$ qty_dot_url <int> 3, 5, 2, 4, 2, 1, 2, 2, 2, 1, 2, …
$ qty_hyphen_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
$ qty_underline_url <int> 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, …
$ qty_slash_url <int> 1, 3, 1, 5, 0, 2, 0, 3, 0, 2, 0, …
$ qty_questionmark_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_equal_url <int> 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_at_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_and_url <int> 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_exclamation_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_space_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_tilde_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_comma_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_plus_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_asterisk_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_hashtag_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_dollar_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_percent_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_tld_url <int> 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ length_url <int> 25, 223, 15, 81, 19, 22, 27, 46, …
$ qty_dot_domain <int> 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, …
$ qty_hyphen_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
$ qty_underline_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_slash_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_questionmark_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_equal_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_at_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_and_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_exclamation_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_space_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_tilde_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_comma_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_plus_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_asterisk_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_hashtag_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_dollar_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_percent_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_vowels_domain <int> 4, 5, 3, 7, 5, 4, 9, 9, 3, 4, 5, …
$ domain_length <int> 17, 16, 14, 19, 19, 17, 27, 28, 1…
$ domain_in_ip <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ server_client_domain <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ qty_dot_directory <int> 1, 3, 0, 2, -1, 0, -1, 0, -1, 0, …
$ qty_hyphen_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_underline_directory <int> 0, 0, 0, 2, -1, 0, -1, 0, -1, 0, …
$ qty_slash_directory <int> 1, 3, 1, 5, -1, 2, -1, 3, -1, 2, …
$ qty_questionmark_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_equal_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_at_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_and_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_exclamation_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_space_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_tilde_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_comma_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_plus_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_asterisk_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_hashtag_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_dollar_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_percent_directory <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ directory_length <int> 8, 42, 1, 62, -1, 5, -1, 18, -1, …
$ qty_dot_file <int> 1, 1, 0, 1, -1, 0, -1, 0, -1, 0, …
$ qty_hyphen_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_underline_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_slash_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_questionmark_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_equal_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_at_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_and_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_exclamation_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_space_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_tilde_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_comma_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_plus_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_asterisk_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_hashtag_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_dollar_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ qty_percent_file <int> 0, 0, 0, 0, -1, 0, -1, 0, -1, 0, …
$ file_length <int> 7, 9, 0, 9, -1, 0, -1, 2, -1, 0, …
$ qty_dot_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_hyphen_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_underline_params <int> -1, 1, -1, -1, -1, -1, -1, -1, -1…
$ qty_slash_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_questionmark_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_equal_params <int> -1, 3, -1, -1, -1, -1, -1, -1, -1…
$ qty_at_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_and_params <int> -1, 2, -1, -1, -1, -1, -1, -1, -1…
$ qty_exclamation_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_space_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_tilde_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_comma_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_plus_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_asterisk_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_hashtag_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_dollar_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_percent_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ params_length <int> -1, 165, -1, -1, -1, -1, -1, -1, …
$ tld_present_params <int> -1, 0, -1, -1, -1, -1, -1, -1, -1…
$ qty_params <int> -1, 3, -1, -1, -1, -1, -1, -1, -1…
$ email_in_url <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ time_response <dbl> 0.207316, 0.499566, 0.935901, 0.4…
$ domain_spf <int> 0, -1, 0, 0, 0, -1, 0, 0, 0, 1, 0…
$ asn_ip <int> 60781, 36024, 4766, 20454, 53831,…
$ time_domain_activation <int> -1, 579, -1, -1, 6998, 9, -1, -1,…
$ time_domain_expiration <int> -1, 150, -1, -1, 306, 355, -1, -1…
$ qty_ip_resolved <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ qty_nameservers <int> 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 2, …
$ qty_mx_servers <int> 0, 1, 3, 0, 1, 3, 2, 1, 2, 1, 1, …
$ ttl_hostname <int> 892, 9540, 589, 292, 3597, 3591, …
$ tls_ssl_certificate <int> 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, …
$ qty_redirects <int> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, …
$ url_google_index <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ domain_google_index <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ url_shortened <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ phishing <int> 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, …
Como podemos ver a través de la función glimpse(), tenemos 88647 registros y 112 variables. Por otra parte todas nuestras variables son números enteros (int) excepto una que es de tipo decimal (dbl) de time_response. Referente a su significado, la mayoría de nuestras predictoras representan cantidades, en este caso, de caracteres en una URL.
Con la función skim() podemos obtener estadísticas básicas sobre nuestros datos de
skim(phishing_bruto)
| Name | phishing_bruto |
| Number of rows | 88647 |
| Number of columns | 112 |
| _______________________ | |
| Column type frequency: | |
| numeric | 112 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| qty_dot_url | 0 | 1 | 2.19 | 1.24 | 1 | 2.00 | 2.00 | 2.00 | 24.0 | ▇▁▁▁▁ |
| qty_hyphen_url | 0 | 1 | 0.33 | 1.12 | 0 | 0.00 | 0.00 | 0.00 | 35.0 | ▇▁▁▁▁ |
| qty_underline_url | 0 | 1 | 0.11 | 0.66 | 0 | 0.00 | 0.00 | 0.00 | 21.0 | ▇▁▁▁▁ |
| qty_slash_url | 0 | 1 | 1.28 | 1.89 | 0 | 0.00 | 0.00 | 2.00 | 44.0 | ▇▁▁▁▁ |
| qty_questionmark_url | 0 | 1 | 0.01 | 0.11 | 0 | 0.00 | 0.00 | 0.00 | 9.0 | ▇▁▁▁▁ |
| qty_equal_url | 0 | 1 | 0.21 | 0.95 | 0 | 0.00 | 0.00 | 0.00 | 23.0 | ▇▁▁▁▁ |
| qty_at_url | 0 | 1 | 0.02 | 0.28 | 0 | 0.00 | 0.00 | 0.00 | 43.0 | ▇▁▁▁▁ |
| qty_and_url | 0 | 1 | 0.14 | 0.92 | 0 | 0.00 | 0.00 | 0.00 | 26.0 | ▇▁▁▁▁ |
| qty_exclamation_url | 0 | 1 | 0.00 | 0.09 | 0 | 0.00 | 0.00 | 0.00 | 10.0 | ▇▁▁▁▁ |
| qty_space_url | 0 | 1 | 0.00 | 0.07 | 0 | 0.00 | 0.00 | 0.00 | 9.0 | ▇▁▁▁▁ |
| qty_tilde_url | 0 | 1 | 0.00 | 0.08 | 0 | 0.00 | 0.00 | 0.00 | 5.0 | ▇▁▁▁▁ |
| qty_comma_url | 0 | 1 | 0.00 | 0.08 | 0 | 0.00 | 0.00 | 0.00 | 11.0 | ▇▁▁▁▁ |
| qty_plus_url | 0 | 1 | 0.00 | 0.11 | 0 | 0.00 | 0.00 | 0.00 | 19.0 | ▇▁▁▁▁ |
| qty_asterisk_url | 0 | 1 | 0.00 | 0.30 | 0 | 0.00 | 0.00 | 0.00 | 60.0 | ▇▁▁▁▁ |
| qty_hashtag_url | 0 | 1 | 0.00 | 0.06 | 0 | 0.00 | 0.00 | 0.00 | 13.0 | ▇▁▁▁▁ |
| qty_dollar_url | 0 | 1 | 0.00 | 0.10 | 0 | 0.00 | 0.00 | 0.00 | 10.0 | ▇▁▁▁▁ |
| qty_percent_url | 0 | 1 | 0.11 | 1.72 | 0 | 0.00 | 0.00 | 0.00 | 174.0 | ▇▁▁▁▁ |
| qty_tld_url | 0 | 1 | 1.05 | 0.25 | 0 | 1.00 | 1.00 | 1.00 | 12.0 | ▇▁▁▁▁ |
| length_url | 0 | 1 | 36.35 | 46.19 | 4 | 17.00 | 22.00 | 38.00 | 4165.0 | ▇▁▁▁▁ |
| qty_dot_domain | 0 | 1 | 1.87 | 0.71 | 0 | 1.00 | 2.00 | 2.00 | 21.0 | ▇▁▁▁▁ |
| qty_hyphen_domain | 0 | 1 | 0.11 | 0.42 | 0 | 0.00 | 0.00 | 0.00 | 11.0 | ▇▁▁▁▁ |
| qty_underline_domain | 0 | 1 | 0.00 | 0.03 | 0 | 0.00 | 0.00 | 0.00 | 3.0 | ▇▁▁▁▁ |
| qty_slash_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_questionmark_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_equal_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_at_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 1.0 | ▇▁▁▁▁ |
| qty_and_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_exclamation_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_space_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_tilde_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_comma_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_plus_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_asterisk_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_hashtag_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_dollar_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_percent_domain | 0 | 1 | 0.00 | 0.00 | 0 | 0.00 | 0.00 | 0.00 | 0.0 | ▁▁▇▁▁ |
| qty_vowels_domain | 0 | 1 | 5.44 | 2.54 | 0 | 4.00 | 5.00 | 7.00 | 61.0 | ▇▁▁▁▁ |
| domain_length | 0 | 1 | 18.56 | 6.60 | 4 | 14.00 | 18.00 | 22.00 | 231.0 | ▇▁▁▁▁ |
| domain_in_ip | 0 | 1 | 0.00 | 0.05 | 0 | 0.00 | 0.00 | 0.00 | 1.0 | ▇▁▁▁▁ |
| server_client_domain | 0 | 1 | 0.00 | 0.07 | 0 | 0.00 | 0.00 | 0.00 | 1.0 | ▇▁▁▁▁ |
| qty_dot_directory | 0 | 1 | -0.32 | 0.90 | -1 | -1.00 | -1.00 | 0.00 | 19.0 | ▇▁▁▁▁ |
| qty_hyphen_directory | 0 | 1 | -0.36 | 1.10 | -1 | -1.00 | -1.00 | 0.00 | 23.0 | ▇▁▁▁▁ |
| qty_underline_directory | 0 | 1 | -0.48 | 0.68 | -1 | -1.00 | -1.00 | 0.00 | 17.0 | ▇▁▁▁▁ |
| qty_slash_directory | 0 | 1 | 0.71 | 2.22 | -1 | -1.00 | -1.00 | 2.00 | 22.0 | ▇▁▁▁▁ |
| qty_questionmark_directory | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_equal_directory | 0 | 1 | -0.53 | 0.52 | -1 | -1.00 | -1.00 | 0.00 | 5.0 | ▇▁▁▁▁ |
| qty_at_directory | 0 | 1 | -0.53 | 0.55 | -1 | -1.00 | -1.00 | 0.00 | 43.0 | ▇▁▁▁▁ |
| qty_and_directory | 0 | 1 | -0.53 | 0.54 | -1 | -1.00 | -1.00 | 0.00 | 26.0 | ▇▁▁▁▁ |
| qty_exclamation_directory | 0 | 1 | -0.53 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 9.0 | ▇▁▁▁▁ |
| qty_space_directory | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 9.0 | ▇▁▁▁▁ |
| qty_tilde_directory | 0 | 1 | -0.53 | 0.51 | -1 | -1.00 | -1.00 | 0.00 | 5.0 | ▇▁▁▁▁ |
| qty_comma_directory | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 5.0 | ▇▁▁▁▁ |
| qty_plus_directory | 0 | 1 | -0.53 | 0.51 | -1 | -1.00 | -1.00 | 0.00 | 19.0 | ▇▁▁▁▁ |
| qty_asterisk_directory | 0 | 1 | -0.53 | 0.58 | -1 | -1.00 | -1.00 | 0.00 | 60.0 | ▇▁▁▁▁ |
| qty_hashtag_directory | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_dollar_directory | 0 | 1 | -0.53 | 0.51 | -1 | -1.00 | -1.00 | 0.00 | 10.0 | ▇▁▁▁▁ |
| qty_percent_directory | 0 | 1 | -0.48 | 1.53 | -1 | -1.00 | -1.00 | 0.00 | 174.0 | ▇▁▁▁▁ |
| directory_length | 0 | 1 | 10.86 | 24.35 | -1 | -1.00 | -1.00 | 16.00 | 1286.0 | ▇▁▁▁▁ |
| qty_dot_file | 0 | 1 | -0.37 | 0.77 | -1 | -1.00 | -1.00 | 0.00 | 12.0 | ▇▁▁▁▁ |
| qty_hyphen_file | 0 | 1 | -0.48 | 0.80 | -1 | -1.00 | -1.00 | 0.00 | 21.0 | ▇▁▁▁▁ |
| qty_underline_file | 0 | 1 | -0.51 | 0.60 | -1 | -1.00 | -1.00 | 0.00 | 17.0 | ▇▁▁▁▁ |
| qty_slash_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_questionmark_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_equal_file | 0 | 1 | -0.53 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 3.0 | ▇▇▁▁▁ |
| qty_at_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 2.0 | ▇▇▁▁▁ |
| qty_and_file | 0 | 1 | -0.53 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 3.0 | ▇▇▁▁▁ |
| qty_exclamation_file | 0 | 1 | -0.53 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 4.0 | ▇▁▁▁▁ |
| qty_space_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 9.0 | ▇▁▁▁▁ |
| qty_tilde_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 4.0 | ▇▁▁▁▁ |
| qty_comma_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 5.0 | ▇▁▁▁▁ |
| qty_plus_file | 0 | 1 | -0.54 | 0.51 | -1 | -1.00 | -1.00 | 0.00 | 19.0 | ▇▁▁▁▁ |
| qty_asterisk_file | 0 | 1 | -0.53 | 0.55 | -1 | -1.00 | -1.00 | 0.00 | 60.0 | ▇▁▁▁▁ |
| qty_hashtag_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_dollar_file | 0 | 1 | -0.54 | 0.50 | -1 | -1.00 | -1.00 | 0.00 | 0.0 | ▇▁▁▁▇ |
| qty_percent_file | 0 | 1 | -0.50 | 1.44 | -1 | -1.00 | -1.00 | 0.00 | 174.0 | ▇▁▁▁▁ |
| file_length | 0 | 1 | 2.74 | 13.57 | -1 | -1.00 | -1.00 | 0.00 | 1232.0 | ▇▁▁▁▁ |
| qty_dot_params | 0 | 1 | -0.81 | 0.98 | -1 | -1.00 | -1.00 | -1.00 | 23.0 | ▇▁▁▁▁ |
| qty_hyphen_params | 0 | 1 | -0.88 | 0.63 | -1 | -1.00 | -1.00 | -1.00 | 35.0 | ▇▁▁▁▁ |
| qty_underline_params | 0 | 1 | -0.86 | 0.66 | -1 | -1.00 | -1.00 | -1.00 | 21.0 | ▇▁▁▁▁ |
| qty_slash_params | 0 | 1 | -0.89 | 0.55 | -1 | -1.00 | -1.00 | -1.00 | 43.0 | ▇▁▁▁▁ |
| qty_questionmark_params | 0 | 1 | -0.91 | 0.32 | -1 | -1.00 | -1.00 | -1.00 | 9.0 | ▇▁▁▁▁ |
| qty_equal_params | 0 | 1 | -0.73 | 1.11 | -1 | -1.00 | -1.00 | -1.00 | 23.0 | ▇▁▁▁▁ |
| qty_at_params | 0 | 1 | -0.90 | 0.36 | -1 | -1.00 | -1.00 | -1.00 | 10.0 | ▇▁▁▁▁ |
| qty_and_params | 0 | 1 | -0.79 | 1.02 | -1 | -1.00 | -1.00 | -1.00 | 22.0 | ▇▁▁▁▁ |
| qty_exclamation_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 10.0 | ▇▁▁▁▁ |
| qty_space_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 4.0 | ▇▁▁▁▁ |
| qty_tilde_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 1.0 | ▇▁▁▁▁ |
| qty_comma_params | 0 | 1 | -0.91 | 0.29 | -1 | -1.00 | -1.00 | -1.00 | 11.0 | ▇▁▁▁▁ |
| qty_plus_params | 0 | 1 | -0.91 | 0.29 | -1 | -1.00 | -1.00 | -1.00 | 6.0 | ▇▁▁▁▁ |
| qty_asterisk_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 4.0 | ▇▁▁▁▁ |
| qty_hashtag_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 0.0 | ▇▁▁▁▁ |
| qty_dollar_params | 0 | 1 | -0.92 | 0.28 | -1 | -1.00 | -1.00 | -1.00 | 4.0 | ▇▁▁▁▁ |
| qty_percent_params | 0 | 1 | -0.86 | 1.05 | -1 | -1.00 | -1.00 | -1.00 | 65.0 | ▇▁▁▁▁ |
| params_length | 0 | 1 | 5.27 | 34.94 | -1 | -1.00 | -1.00 | -1.00 | 4094.0 | ▇▁▁▁▁ |
| tld_present_params | 0 | 1 | -0.89 | 0.38 | -1 | -1.00 | -1.00 | -1.00 | 1.0 | ▇▁▁▁▁ |
| qty_params | 0 | 1 | -0.76 | 0.94 | -1 | -1.00 | -1.00 | -1.00 | 23.0 | ▇▁▁▁▁ |
| email_in_url | 0 | 1 | 0.02 | 0.13 | 0 | 0.00 | 0.00 | 0.00 | 1.0 | ▇▁▁▁▁ |
| time_response | 0 | 1 | 0.79 | 1.47 | -1 | 0.24 | 0.47 | 0.87 | 38.4 | ▇▁▁▁▁ |
| domain_spf | 0 | 1 | -0.02 | 0.56 | -1 | 0.00 | 0.00 | 0.00 | 1.0 | ▂▁▇▁▂ |
| asn_ip | 0 | 1 | 31131.15 | 45261.50 | -1 | 13335.00 | 20013.00 | 34922.00 | 395754.0 | ▇▁▁▁▁ |
| time_domain_activation | 0 | 1 | 3389.68 | 3044.17 | -1 | -1.00 | 3046.00 | 6423.00 | 17775.0 | ▇▅▂▁▁ |
| time_domain_expiration | 0 | 1 | 352.04 | 598.26 | -1 | -1.00 | 168.00 | 354.00 | 22574.0 | ▇▁▁▁▁ |
| qty_ip_resolved | 0 | 1 | 1.14 | 0.90 | -1 | 1.00 | 1.00 | 1.00 | 24.0 | ▇▁▁▁▁ |
| qty_nameservers | 0 | 1 | 2.77 | 1.32 | 0 | 2.00 | 2.00 | 4.00 | 20.0 | ▇▁▁▁▁ |
| qty_mx_servers | 0 | 1 | 1.74 | 1.71 | 0 | 1.00 | 1.00 | 2.00 | 20.0 | ▇▁▁▁▁ |
| ttl_hostname | 0 | 1 | 6159.88 | 11465.58 | -1 | 292.00 | 2029.00 | 10798.00 | 604800.0 | ▇▁▁▁▁ |
| tls_ssl_certificate | 0 | 1 | 0.51 | 0.50 | 0 | 0.00 | 1.00 | 1.00 | 1.0 | ▇▁▁▁▇ |
| qty_redirects | 0 | 1 | 0.34 | 0.78 | -1 | 0.00 | 0.00 | 1.00 | 17.0 | ▇▁▁▁▁ |
| url_google_index | 0 | 1 | 0.00 | 0.06 | -1 | 0.00 | 0.00 | 0.00 | 1.0 | ▁▁▇▁▁ |
| domain_google_index | 0 | 1 | 0.00 | 0.06 | -1 | 0.00 | 0.00 | 0.00 | 1.0 | ▁▁▇▁▁ |
| url_shortened | 0 | 1 | 0.01 | 0.07 | 0 | 0.00 | 0.00 | 0.00 | 1.0 | ▇▁▁▁▁ |
| phishing | 0 | 1 | 0.35 | 0.48 | 0 | 0.00 | 0.00 | 1.00 | 1.0 | ▇▁▁▁▅ |
En una primera vista, podemos observar que en nuestras variables no tenemos ningún valor ausente.
De este modo, el complete_rate de nuestras variables es siempre = 1. Por el otro lado, la función
también nos muestra diferentes estadísticos descriptivos de nuestras variables, como los percentiles,
media y desviación típica. Fijándonos en sus valores, y teniendo en cuenta que la mayoría de nuestras
variables hacen referencia a cantidades de caracteres o a tiempos de espera de una página web,
era de esperar que no tengamos demasiada dispersión en nuestros datos.
Por último, podemos ver como está distribuída nuestra variable objetivo dentro de nuestro conjunto de datos.
phishing n porc
1 0 58000 65.42805
2 1 30647 34.57195
Como podemos ver, nuestra variable objetivo es de tipo binario, tomando valores 0 y 1. En este caso, el 1 nos indica que la URL en cuestión se corresponde a una web destinada al phishing y cuando toma el valor 0, que esto no es así. Si nos fijamos en sus proporciones, vemos que en el 65,4% de los casos, la página web no es de phishing, y en el 34,6% sí lo es. Por otra parte, esta variable representa una categoría, por lo que antes de comenzar con nuestra metodología, es conveniente transformar esta variable previamente a factor para poder realizar una buena exploración.
Comenzamos con las 3 primeras fases de la metodología SEMMA.
La primera pregunta que debemos hacernos es, ¿Necesitamos hacer una muestra de nuestros datos?. Teniendo en cuenta que tenemos unos 89 mil registros, no realizaremos un muestreo estratificado inicial, ya que tenemos una cantidad de datos fácilmente manejable. Podríamos llegar a necesitarlo si contasemos con millones de registros, lo que nos permitiría trabajar con mayor fluidez. Por lo tanto, nos saltaremos esta primera fase.
Procedemos a analizar las variables dividiéndolas en grupos según su naturaleza.
Primero analizaremos las variables que reflejan datos acerca de la propia URL.
Esta variable representa la cantidad de puntos (.) presentes en la URL.
# A tibble: 33 × 4
# Groups: qty_dot_url [23]
qty_dot_url phishing n porc
<int> <fct> <int> <dbl>
1 1 0 8365 52.7
2 1 1 7520 47.3
3 2 0 41088 76.6
4 2 1 12561 23.4
5 3 0 6768 51.3
6 3 1 6437 48.7
7 4 0 1609 44.8
8 4 1 1984 55.2
9 5 0 145 14.4
10 5 1 863 85.6
# … with 23 more rows
qty_dot_url n porc cumul
1 1 15885 17.919388135 17.91939
2 2 53649 60.519814545 78.43920
3 3 13205 14.896161179 93.33536
4 4 3593 4.053154647 97.38852
5 5 1008 1.137094318 98.52561
6 6 454 0.512143671 99.03776
7 7 217 0.244791138 99.28255
8 8 120 0.135368371 99.41792
9 9 64 0.072196465 99.49011
10 10 48 0.054147348 99.54426
11 11 32 0.036098232 99.58036
12 12 33 0.037226302 99.61758
13 13 37 0.041738581 99.65932
14 14 135 0.152289418 99.81161
15 15 86 0.097013999 99.90863
16 16 44 0.049635069 99.95826
17 17 22 0.024817535 99.98308
18 18 6 0.006768419 99.98985
19 19 1 0.001128070 99.99098
20 20 3 0.003384209 99.99436
21 22 3 0.003384209 99.99774
22 23 1 0.001128070 99.99887
23 24 1 0.001128070 100.00000
Como vemos, la variable toma valores entre 1 y 23 y la gran mayoría de sus niveles no presentan apenas el 1% de los datos totales.
Podemos realizar un gráfico de barras para ver las distribuciones con la objetivo
# Representación gráfica
ggplot(phishing_bruto, aes(x = qty_dot_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
geom_text(aes(label = ..count..), stat = "count", vjust = -0.5) +
labs(title = "Webs de Phishing por número de puntos en la URL",
x = "Nº Puntos", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Representativa del número de guiones (-) en la URL.
# A tibble: 51 × 4
# Groups: qty_hyphen_url [30]
qty_hyphen_url phishing n porc
<int> <fct> <int> <dbl>
1 0 0 52962 72.5
2 0 1 20104 27.5
3 1 0 3676 34.8
4 1 1 6897 65.2
5 2 0 643 26.4
6 2 1 1793 73.6
7 3 0 162 18.0
8 3 1 739 82.0
9 4 0 88 17.2
10 4 1 424 82.8
# … with 41 more rows
Siguiendo con la lógica anterior, volvemos a convertir la variable a factor
phishing_bruto <-
phishing_bruto |> mutate(qty_hyphen_url = as_factor(qty_hyphen_url))
# Representación
ggplot(phishing_bruto, aes(x = qty_hyphen_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de guiones en la URL",
x = "Nº Guiones", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
La mayoría de los datos se concentran en el 0 donde la cantidad de páginas web legítimas es superior a las proporciones globales. Cuando hay más de un guión en la URL, la proporción de páginas web de phishing es superior, con unos porcentajes superiores al 65%. Existen también ciertos niveles donde la proporción de legítimas es superior, pero dado que existen muy pocos registros cuando ya contamos con un número elevado de guiones, sería mejor recategorizarlos. Atendiendo a sus proporciones y registros, y siguiendo con la lógica anterior, podemos crear dos niveles (variable binaria) haciendo nuevamente una recategorización de
Una vez llegados a este punto, y viendo que las demás variables referidas a cantidades de caracteres en las URL’s pueden seguir la misma lógica en el análisis, podriamos aplicar la misma técnica exploratoria de forma conjunta para el resto de ellas, y continuando con el método anterior, sacar posibles recategorizaciones / transformaciones en sus niveles o codificación.
Diccionario de las variables de
Comprobamos el número de valores únicos en estas variables:
phishing_bruto |> select(contains("URL")) |>
summarise_all(list(~n_distinct(.)))
qty_dot_url qty_hyphen_url qty_underline_url qty_slash_url
1 23 30 22 25
qty_questionmark_url qty_equal_url qty_at_url qty_and_url
1 6 20 15 21
qty_exclamation_url qty_space_url qty_tilde_url qty_comma_url
1 10 8 6 8
qty_plus_url qty_asterisk_url qty_hashtag_url qty_dollar_url
1 10 18 7 10
qty_percent_url qty_tld_url length_url email_in_url
1 52 9 453 2
url_google_index url_shortened
1 3 2
Convertimos las variables a factor:
phishing_bruto <-
phishing_bruto |> mutate(qty_underline_url = as_factor(qty_underline_url),
qty_slash_url = as_factor(qty_slash_url),
qty_questionmark_url = as_factor(qty_questionmark_url),
qty_equal_url = as_factor(qty_equal_url),
qty_at_url = as_factor(qty_at_url),
qty_and_url = as_factor(qty_and_url),
qty_exclamation_url = as_factor(qty_exclamation_url),
qty_space_url = as_factor(qty_space_url),
qty_tilde_url = as_factor(qty_tilde_url),
qty_comma_url = as_factor(qty_comma_url),
qty_plus_url = as_factor(qty_plus_url),
qty_asterisk_url = as_factor(qty_asterisk_url),
qty_hashtag_url = as_factor(qty_hashtag_url),
qty_dollar_url = as_factor(qty_dollar_url),
qty_tld_url = as_factor(qty_tld_url),
email_in_url = as_factor(email_in_url),
url_google_index = as_factor(url_google_index),
url_shortened = as_factor(url_shortened))
Realizaremos nuestro análisis en grupos de variables que representan cantidades de caracteres específicos (qty), y después trataremos las demás variables URL de diferente naturaleza.
Para variables como length_url y qty_percent_url, evitaremos convertirla en factor para nuestro análisis, ya que toma muchos valores únicos. De esta forma, podremos hacer gráficos que proporcionan una mejor visualización para este tipo de variables, como histogramas. Una vez analizada, decidiremos si posteriormente convendría convertirla en factor también.
Análisis cuantitativo
# qty_underline_url
phishing_bruto |> group_by(qty_underline_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 35 × 4
# Groups: qty_underline_url [22]
qty_underline_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57298 68.8
2 0 1 26020 31.2
3 1 0 416 12.6
4 1 1 2890 87.4
5 2 0 144 13.8
6 2 1 896 86.2
7 3 0 75 15.8
8 3 1 400 84.2
9 4 0 26 12.6
10 4 1 180 87.4
# … with 25 more rows
# qty_slash_url
phishing_bruto |> group_by(qty_slash_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 36 × 4
# Groups: qty_slash_url [25]
qty_slash_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 46762 98.4
2 0 1 747 1.57
3 1 0 7045 52.1
4 1 1 6477 47.9
5 2 0 2317 28.7
6 2 1 5755 71.3
7 3 0 1223 14.6
8 3 1 7135 85.4
9 4 0 293 6.27
10 4 1 4383 93.7
# … with 26 more rows
# qty_questionmark_url
phishing_bruto |> group_by(qty_questionmark_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 8 × 4
# Groups: qty_questionmark_url [6]
qty_questionmark_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57989 66.0
2 0 1 29909 34.0
3 1 0 11 1.59
4 1 1 682 98.4
5 2 1 50 100
6 3 1 3 100
7 7 1 1 100
8 9 1 2 100
# qty_equal_url
phishing_bruto |> group_by(qty_equal_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 34 × 4
# Groups: qty_equal_url [20]
qty_equal_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57304 70.5
2 0 1 24035 29.5
3 1 0 390 11.1
4 1 1 3124 88.9
5 2 0 121 9.75
6 2 1 1120 90.2
7 3 0 121 9.10
8 3 1 1208 90.9
9 4 0 40 14.9
10 4 1 229 85.1
# … with 24 more rows
Visualización:
# qty_underline_url
ggplot(phishing_bruto, aes(x = qty_underline_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (_) en la URL",
x = "Nº de (_)", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_slash_url
ggplot(phishing_bruto, aes(x = qty_slash_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (/) en la URL",
x = "Nº de (/)", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal() + coord_flip()
# qty_questionmark_url
ggplot(phishing_bruto, aes(x = qty_questionmark_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (?) en la URL",
x = "Nº de (?)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = ..count..), stat = "count", vjust = 1, hjust = -0.2) +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal() + coord_flip()
# qty_equal_url
ggplot(phishing_bruto, aes(x = qty_equal_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (=) en la URL",
x = "Nº de (=)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_underline_url: La distribución con la objetivo es muy similar a la variable de número de
guiones. Cuando las “barra bajas” son 0 en la URL, la proporción de páginas web legítimas es
ligeramente superior a la global, y cuando es mayor que 0, la proporción de phishing es considerablemente
superior y contamos con muy pocos registros -> Recategorizamos: 0, >0.
qty_slash_url: Si nos fijamos en sus proporciones, podemos distinguir 4 tramos diferenciales,
ya que cuando el numero de (/) es 0, no tenemos apenas webs de phishing. Cuando encontramos un (/),
el porcentaje de phishing asciende al 47%, cuando es = 2 tenemos un 71% y cuando son 3, la proporción
es del 85%. A partir de ese valor, el número de registros es considerablemente menor como para ser
reagrupado en una categoría conjunta donde la proporción de phishing estaría sobre el 90%. Teniendo
esto en cuenta, podríamos reagrupar estos últimos niveles junto con el número de (/) = 3, y de esta
forma tener 4 niveles con proporciones diferenciadas: 0, 1, 2, >2.
qty_questionmark_url: Dadas sus proporciones, claramente convendría transformar esta variable
en binaria, con los siguiente niveles: 0, >0.
qty_equal_url: Encontramos dos tramos diferenciales: cuando el número de (=) es 0, la proporción
de webs legítimas es del 70%, y cuando aparece almenos uno, el porcentaje desciende en torno al 10%.
Una vez más, tenemos muchos más niveles con muy pocos registros donde el número de webs de phishing
predomina. De este modo, y siguiendo con la lógica anterior, crearemos dos niveles recategorizando:
0, >0.
Análisis cuantitativo:
# qty_at_url
phishing_bruto |> group_by(qty_at_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 17 × 4
# Groups: qty_at_url [15]
qty_at_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57998 66.7
2 0 1 28939 33.3
3 1 0 2 0.120
4 1 1 1659 99.9
5 2 1 20 100
6 3 1 6 100
7 4 1 7 100
8 5 1 3 100
9 6 1 2 100
10 7 1 1 100
11 8 1 1 100
12 10 1 2 100
13 11 1 1 100
14 12 1 1 100
15 14 1 1 100
16 15 1 2 100
17 43 1 2 100
# qty_and_url
phishing_bruto |> group_by(qty_and_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 34 × 4
# Groups: qty_and_url [21]
qty_and_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57691 68.0
2 0 1 27158 32.0
3 1 0 121 10.3
4 1 1 1054 89.7
5 2 0 122 9.28
6 2 1 1193 90.7
7 3 0 42 16.2
8 3 1 217 83.8
9 4 0 5 3.62
10 4 1 133 96.4
# … with 24 more rows
# qty_exclamation_url
phishing_bruto |> group_by(qty_exclamation_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 12 × 4
# Groups: qty_exclamation_url [10]
qty_exclamation_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57950 65.5
2 0 1 30504 34.5
3 1 0 50 29.9
4 1 1 117 70.1
5 2 1 16 100
6 3 1 2 100
7 4 1 1 100
8 5 1 2 100
9 6 1 1 100
10 8 1 1 100
11 9 1 2 100
12 10 1 1 100
# qty_space_url
phishing_bruto |> group_by(qty_space_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 9 × 4
# Groups: qty_space_url [8]
qty_space_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.5
2 0 1 30613 34.5
3 1 1 20 100
4 2 1 3 100
5 3 1 3 100
6 4 1 2 100
7 6 1 1 100
8 7 1 2 100
9 9 1 3 100
Visualización:
# qty_at_url
ggplot(phishing_bruto, aes(x = qty_at_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (@) en la URL",
x = "Nº de (@)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_and_url
ggplot(phishing_bruto, aes(x = qty_and_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (&) en la URL",
x = "Nº de (&)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_exclamation_url
ggplot(phishing_bruto, aes(x = qty_exclamation_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (!) en la URL",
x = "Nº de (!)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_space_url
ggplot(phishing_bruto, aes(x = qty_space_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de espacios en la URL",
x = "Nº de espacios", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_at_url: Casi la totalidad de los registros está en el nivel 0 donde las proporciones son
similares a las globales en cuanto a la objetivo. A partir de este valor, aparte de apenas tener
observaciones, el porcentaje de phishing es muy elevado, por lo que convertiremos esta variable en
binaria: 0, >0.qty_and_url: Relativo a sus proporciones de phishing y legítimas y al número de registros, sucede
lo mismo que con la anterior variable. Aplicamos la misma lógica: 0, >0.qty_exclamation_url: Sucede lo mismo que en los casos anteriores: 0, >0.qty_space_url: Una vez más: 0, >0Análisis cuantitativo:
# qty_tilde_url
phishing_bruto |> group_by(qty_tilde_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 10 × 4
# Groups: qty_tilde_url [6]
qty_tilde_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57986 65.6
2 0 1 30444 34.4
3 1 0 12 6.42
4 1 1 175 93.6
5 2 1 5 100
6 3 0 1 5.88
7 3 1 16 94.1
8 4 0 1 50
9 4 1 1 50
10 5 1 6 100
# qty_comma_url
phishing_bruto |> group_by(qty_comma_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 11 × 4
# Groups: qty_comma_url [8]
qty_comma_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57986 65.5
2 0 1 30537 34.5
3 1 0 4 4.12
4 1 1 93 95.9
5 2 1 2 100
6 3 0 10 50
7 3 1 10 50
8 4 1 2 100
9 5 1 1 100
10 7 1 1 100
11 11 1 1 100
# qty_plus_url
phishing_bruto |> group_by(qty_plus_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 15 × 4
# Groups: qty_plus_url [10]
qty_plus_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57931 65.4
2 0 1 30587 34.6
3 1 0 57 64.0
4 1 1 32 36.0
5 2 0 5 29.4
6 2 1 12 70.6
7 3 1 7 100
8 4 0 2 40
9 4 1 3 60
10 5 0 1 33.3
11 5 1 2 66.7
12 6 1 4 100
13 7 0 1 100
14 9 0 2 100
15 19 0 1 100
# qty_asterisk_url
phishing_bruto |> group_by(qty_asterisk_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 19 × 4
# Groups: qty_asterisk_url [18]
qty_asterisk_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.5
2 0 1 30588 34.5
3 1 1 18 100
4 2 1 11 100
5 3 1 3 100
6 4 1 6 100
7 5 1 1 100
8 6 1 1 100
9 7 1 1 100
10 8 1 2 100
11 9 1 3 100
12 10 1 1 100
13 12 1 1 100
14 14 1 3 100
15 19 1 1 100
16 20 1 3 100
17 21 1 1 100
18 22 1 2 100
19 60 1 1 100
Visualización:
# qty_tilde_url
ggplot(phishing_bruto, aes(x = qty_tilde_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (~) en la URL",
x = "Nº de (~)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = ..count..), stat = "count", vjust = 1, hjust = -0.2) +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal() + coord_flip()
# qty_comma_url
ggplot(phishing_bruto, aes(x = qty_comma_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (,) en la URL",
x = "Nº de (,)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_plus_url
ggplot(phishing_bruto, aes(x = qty_plus_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (+) en la URL",
x = "Nº de (+)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_tilde_url: 0, >0.qty_comma_url: 0, >0.qty_plus_url: En este caso podriamos reagrupar los niveles 1 y 2 con el 0, ya que aunque no existan
casi registros cuando el número de (+) es mayor que 0, si que vemos más presencia de webs legítimas
en los demás niveles: 0-2, >2.qty_asterisk_url: 0, >00.Análisis cuantitativo:
# qty_hashtag_url
phishing_bruto |> group_by(qty_hashtag_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 8 × 4
# Groups: qty_hashtag_url [7]
qty_hashtag_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30636 34.6
3 1 1 3 100
4 2 1 2 100
5 3 1 3 100
6 7 1 1 100
7 9 1 1 100
8 13 1 1 100
# qty_dollar_url
phishing_bruto |> group_by(qty_dollar_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 11 × 4
# Groups: qty_dollar_url [10]
qty_dollar_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.5
2 0 1 30593 34.5
3 1 1 22 100
4 2 1 4 100
5 3 1 12 100
6 4 1 5 100
7 5 1 3 100
8 6 1 2 100
9 8 1 2 100
10 9 1 1 100
11 10 1 3 100
# qty_percent_url
phishing_bruto |> count(qty_percent_url) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_percent_url n porc cumul
1 0 87111 98.267284849 98.26728
2 1 613 0.691506763 98.95879
3 2 244 0.275249021 99.23404
4 3 86 0.097013999 99.33105
5 4 83 0.093629790 99.42468
6 5 82 0.092501720 99.51719
7 6 65 0.073324534 99.59051
8 7 19 0.021433325 99.61194
9 8 37 0.041738581 99.65368
10 9 42 0.047378930 99.70106
11 10 26 0.029329814 99.73039
12 11 4 0.004512279 99.73490
13 12 26 0.029329814 99.76423
14 13 6 0.006768419 99.77100
15 14 17 0.019177186 99.79018
16 15 12 0.013536837 99.80372
17 16 25 0.028201744 99.83192
18 17 6 0.006768419 99.83869
19 18 21 0.023689465 99.86238
20 19 6 0.006768419 99.86914
21 20 17 0.019177186 99.88832
22 21 6 0.006768419 99.89509
23 22 13 0.014664907 99.90975
24 23 3 0.003384209 99.91314
25 24 5 0.005640349 99.91878
26 26 6 0.006768419 99.92555
27 27 3 0.003384209 99.92893
28 28 13 0.014664907 99.94360
29 29 2 0.002256140 99.94585
30 30 2 0.002256140 99.94811
31 32 8 0.009024558 99.95713
32 34 5 0.005640349 99.96277
33 35 1 0.001128070 99.96390
34 36 3 0.003384209 99.96729
35 38 3 0.003384209 99.97067
36 39 1 0.001128070 99.97180
37 40 1 0.001128070 99.97293
38 42 1 0.001128070 99.97405
39 44 2 0.002256140 99.97631
40 46 1 0.001128070 99.97744
41 48 1 0.001128070 99.97857
42 51 5 0.005640349 99.98421
43 52 1 0.001128070 99.98534
44 54 1 0.001128070 99.98646
45 62 3 0.003384209 99.98985
46 65 1 0.001128070 99.99098
47 66 2 0.002256140 99.99323
48 78 2 0.002256140 99.99549
49 117 1 0.001128070 99.99662
50 127 1 0.001128070 99.99774
51 162 1 0.001128070 99.99887
52 174 1 0.001128070 100.00000
# qty_tld_url
phishing_bruto |> group_by(qty_tld_url) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 12 × 4
# Groups: qty_tld_url [9]
qty_tld_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 26 11.3
2 0 1 204 88.7
3 1 0 57497 68.1
4 1 1 26974 31.9
5 2 0 477 13.5
6 2 1 3058 86.5
7 3 1 352 100
8 4 1 51 100
9 5 1 4 100
10 7 1 2 100
11 9 1 1 100
12 12 1 1 100
Visualización:
# qty_hashtag_url
ggplot(phishing_bruto, aes(x = qty_hashtag_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (#) en la URL",
x = "Nº de (#)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_dollar_url
ggplot(phishing_bruto, aes(x = qty_dollar_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de ($) en la URL",
x = "Nº de ($)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_percent_url
ggplot(phishing_bruto, aes(x = qty_percent_url)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de (%) en la URL",
x = "Nº de (%)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_tld_url
ggplot(phishing_bruto, aes(x = qty_tld_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por número de caracteres de dominio en la URL",
x = "Nº de caracteres de dominio", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = ..count..), stat = "count", vjust = -0.5, hjust = 0.5) +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_hashtag_url: Atendiendo a sus proporciones: 0, >0.qty_dollar_url: 0, >0.qty_percent_url: En esta variable tenemos una mayor cantidad de valores únicos. La gran mayoría
de los datos se concentran en el nivel 0, donde el porcentaje total sobre los datos
es cercano al 100%. A partir de este valor, todos los valores que puede tomar esta
variable apenas concentra el 1% de los datos: phishing n porc
1 0 57706 66.24422
2 1 29405 33.75578
Por otra parte, en este nivel donde tenemos casi la totalidad de los registros, las proporciones en relación a la objetivo son prácticamente idénticas a las globales, por lo que no obtendríamos información relevante para nuestro modelo. Teniendo en cuenta esto, convendría eliminar esta variable.
qty_tld_url: En esta variable podemos ver que cuando la longitud de el dominio en la URL es
distinto de 1, la probabilidad de que la web sea de phishing es muy superior: 1, ≠1.Esta variable define la longitud de la URL, y no se trata de una variable que representa catidad de caracteres de un objeto en concreto como las anteriores.
# Anañlisis cuantitativo
phishing_bruto |> count(length_url) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
length_url n porc cumul
1 4 2 0.002256140 0.00225614
2 5 21 0.023689465 0.02594560
3 6 73 0.082349092 0.10829470
4 7 175 0.197412208 0.30570690
5 8 287 0.323756021 0.62946293
6 9 479 0.540345415 1.16980834
7 10 987 1.113404853 2.28321319
8 11 1547 1.745123918 4.02833711
9 12 2652 2.991641003 7.01997812
10 13 2758 3.111216398 10.13119451
11 14 3427 3.865895067 13.99708958
12 15 3764 4.246054576 18.24314416
13 16 4206 4.744661410 22.98780557
14 17 4412 4.977043780 27.96484935
15 18 4607 5.197017384 33.16186673
16 19 4544 5.125948989 38.28781572
17 20 4382 4.943201688 43.23101741
18 21 3829 4.319379110 47.55039652
19 22 3281 3.701196882 51.25159340
20 23 2936 3.312012815 54.56360621
21 24 2478 2.795356865 57.35896308
22 25 2093 2.361050007 59.72001309
23 26 1853 2.090313265 61.81032635
24 27 1674 1.888388778 63.69871513
25 28 1427 1.609755547 65.30847068
26 29 1265 1.427008246 66.73547892
27 30 1120 1.263438131 67.99891705
28 31 939 1.059257504 69.05817456
29 32 807 0.910352296 69.96852685
30 33 816 0.920504924 70.88903178
31 34 766 0.864101436 71.75313321
32 35 724 0.816722506 72.56985572
33 36 743 0.838155832 73.40801155
34 37 749 0.844924250 74.25293580
35 38 671 0.756934809 75.00987061
36 39 733 0.826875134 75.83674574
37 40 717 0.808826018 76.64557176
38 41 611 0.689250623 77.33482239
39 42 550 0.620438368 77.95526075
40 43 590 0.665561158 78.62082191
41 44 513 0.578699787 79.19952170
42 45 556 0.627206786 79.82672848
43 46 739 0.833643553 80.66037204
44 47 571 0.644127833 81.30449987
45 48 441 0.497478764 81.80197863
46 49 512 0.577571717 82.37955035
47 50 444 0.500862973 82.88041332
48 51 469 0.529064717 83.40947804
49 52 394 0.444459485 83.85393753
50 53 336 0.379031439 84.23296897
51 54 379 0.427538439 84.66050741
52 55 355 0.400464765 85.06097217
53 56 339 0.382415649 85.44338782
54 57 325 0.366622672 85.81001049
55 58 320 0.360982323 86.17099281
56 59 306 0.345189347 86.51618216
57 60 301 0.339548998 86.85573116
58 61 320 0.360982323 87.21671348
59 62 389 0.438819137 87.65553262
60 63 329 0.371134951 88.02666757
61 64 394 0.444459485 88.47112705
62 65 307 0.346317416 88.81744447
63 66 266 0.300066556 89.11751103
64 67 388 0.437691067 89.55520209
65 68 218 0.245919208 89.80112130
66 69 218 0.245919208 90.04704051
67 70 230 0.259456045 90.30649655
68 71 224 0.252687626 90.55918418
69 72 259 0.292170068 90.85135425
70 73 231 0.260584115 91.11193836
71 74 257 0.289913928 91.40185229
72 75 212 0.239150789 91.64100308
73 76 171 0.192899929 91.83390301
74 77 172 0.194027999 92.02793101
75 78 208 0.234638510 92.26256952
76 79 167 0.188387650 92.45095717
77 80 144 0.162442045 92.61339921
78 81 135 0.152289418 92.76568863
79 82 126 0.142136790 92.90782542
80 83 150 0.169210464 93.07703588
81 84 132 0.148905208 93.22594109
82 85 84 0.094757860 93.32069895
83 86 98 0.110550836 93.43124979
84 87 102 0.115063116 93.54631290
85 88 88 0.099270139 93.64558304
86 89 120 0.135368371 93.78095141
87 90 90 0.101526278 93.88247769
88 91 98 0.110550836 93.99302853
89 92 109 0.122959604 94.11598813
90 93 115 0.129728022 94.24571616
91 94 99 0.111678906 94.35739506
92 95 87 0.098142069 94.45553713
93 96 92 0.103782418 94.55931955
94 97 67 0.075580674 94.63490022
95 98 75 0.084605232 94.71950545
96 99 72 0.081221023 94.80072648
97 100 65 0.073324534 94.87405101
98 101 63 0.071068395 94.94511941
99 102 78 0.087989441 95.03310885
100 103 66 0.074452604 95.10756145
101 104 89 0.100398209 95.20795966
102 105 76 0.085733302 95.29369296
103 106 70 0.078964883 95.37265785
104 107 49 0.055275418 95.42793326
105 108 59 0.066556116 95.49448938
106 109 55 0.062043837 95.55653322
107 110 80 0.090245581 95.64677880
108 111 54 0.060915767 95.70769456
109 112 70 0.078964883 95.78665945
110 113 57 0.064299976 95.85095942
111 114 85 0.095885930 95.94684535
112 115 59 0.066556116 96.01340147
113 116 63 0.071068395 96.08446986
114 117 37 0.041738581 96.12620844
115 118 40 0.045122790 96.17133124
116 119 45 0.050763139 96.22209437
117 120 51 0.057531558 96.27962593
118 121 30 0.033842093 96.31346802
119 122 27 0.030457884 96.34392591
120 123 52 0.058659628 96.40258554
121 124 31 0.034970163 96.43755570
122 125 29 0.032714023 96.47026972
123 126 62 0.069940325 96.54021005
124 127 31 0.034970163 96.57518021
125 128 36 0.040610511 96.61579072
126 129 69 0.077836813 96.69362753
127 130 35 0.039482442 96.73310998
128 131 42 0.047378930 96.78048891
129 132 27 0.030457884 96.81094679
130 133 32 0.036098232 96.84704502
131 134 24 0.027073674 96.87411870
132 135 25 0.028201744 96.90232044
133 136 28 0.031585953 96.93390639
134 137 17 0.019177186 96.95308358
135 138 15 0.016921046 96.97000463
136 139 28 0.031585953 97.00159058
137 140 21 0.023689465 97.02528004
138 141 30 0.033842093 97.05912214
139 142 24 0.027073674 97.08619581
140 143 56 0.063171907 97.14936772
141 144 40 0.045122790 97.19449051
142 145 31 0.034970163 97.22946067
143 146 27 0.030457884 97.25991855
144 147 31 0.034970163 97.29488872
145 148 24 0.027073674 97.32196239
146 149 28 0.031585953 97.35354834
147 150 23 0.025945604 97.37949395
148 151 15 0.016921046 97.39641499
149 152 17 0.019177186 97.41559218
150 153 13 0.014664907 97.43025709
151 154 15 0.016921046 97.44717813
152 155 21 0.023689465 97.47086760
153 156 14 0.015792977 97.48666058
154 157 18 0.020305256 97.50696583
155 158 9 0.010152628 97.51711846
156 159 12 0.013536837 97.53065530
157 160 25 0.028201744 97.55885704
158 161 16 0.018049116 97.57690616
159 162 12 0.013536837 97.59044299
160 163 15 0.016921046 97.60736404
161 164 19 0.021433325 97.62879736
162 165 10 0.011280698 97.64007806
163 166 15 0.016921046 97.65699911
164 167 4 0.004512279 97.66151139
165 168 12 0.013536837 97.67504822
166 169 19 0.021433325 97.69648155
167 170 11 0.012408767 97.70889032
168 171 18 0.020305256 97.72919557
169 172 8 0.009024558 97.73822013
170 173 18 0.020305256 97.75852539
171 174 14 0.015792977 97.77431836
172 175 14 0.015792977 97.79011134
173 176 12 0.013536837 97.80364818
174 177 12 0.013536837 97.81718501
175 178 15 0.016921046 97.83410606
176 179 19 0.021433325 97.85553939
177 180 13 0.014664907 97.87020429
178 181 6 0.006768419 97.87697271
179 182 14 0.015792977 97.89276569
180 183 9 0.010152628 97.90291832
181 184 6 0.006768419 97.90968674
182 185 7 0.007896488 97.91758322
183 186 13 0.014664907 97.93224813
184 187 49 0.055275418 97.98752355
185 188 1 0.001128070 97.98865162
186 189 8 0.009024558 97.99767618
187 190 16 0.018049116 98.01572529
188 191 9 0.010152628 98.02587792
189 192 16 0.018049116 98.04392704
190 193 6 0.006768419 98.05069546
191 194 16 0.018049116 98.06874457
192 195 19 0.021433325 98.09017790
193 196 9 0.010152628 98.10033052
194 197 14 0.015792977 98.11612350
195 198 11 0.012408767 98.12853227
196 199 19 0.021433325 98.14996559
197 200 18 0.020305256 98.17027085
198 201 19 0.021433325 98.19170417
199 202 26 0.029329814 98.22103399
200 203 23 0.025945604 98.24697959
201 204 26 0.029329814 98.27630941
202 205 18 0.020305256 98.29661466
203 206 42 0.047378930 98.34399359
204 207 36 0.040610511 98.38460410
205 208 6 0.006768419 98.39137252
206 209 11 0.012408767 98.40378129
207 210 30 0.033842093 98.43762338
208 211 13 0.014664907 98.45228829
209 212 5 0.005640349 98.45792864
210 213 17 0.019177186 98.47710582
211 214 8 0.009024558 98.48613038
212 215 10 0.011280698 98.49741108
213 216 10 0.011280698 98.50869178
214 217 9 0.010152628 98.51884441
215 218 19 0.021433325 98.54027773
216 219 15 0.016921046 98.55719878
217 220 9 0.010152628 98.56735141
218 221 21 0.023689465 98.59104087
219 222 14 0.015792977 98.60683385
220 223 8 0.009024558 98.61585840
221 224 7 0.007896488 98.62375489
222 225 10 0.011280698 98.63503559
223 226 14 0.015792977 98.65082857
224 227 18 0.020305256 98.67113382
225 228 16 0.018049116 98.68918294
226 229 113 0.127471883 98.81665482
227 230 39 0.043994721 98.86064954
228 231 11 0.012408767 98.87305831
229 232 10 0.011280698 98.88433901
230 233 15 0.016921046 98.90126005
231 234 19 0.021433325 98.92269338
232 235 12 0.013536837 98.93623022
233 236 13 0.014664907 98.95089512
234 237 29 0.032714023 98.98360915
235 238 67 0.075580674 99.05918982
236 239 21 0.023689465 99.08287929
237 240 8 0.009024558 99.09190384
238 241 16 0.018049116 99.10995296
239 242 94 0.106038557 99.21599152
240 243 15 0.016921046 99.23291256
241 244 12 0.013536837 99.24644940
242 245 11 0.012408767 99.25885817
243 246 12 0.013536837 99.27239500
244 247 11 0.012408767 99.28480377
245 248 10 0.011280698 99.29608447
246 249 6 0.006768419 99.30285289
247 250 14 0.015792977 99.31864587
248 251 7 0.007896488 99.32654235
249 252 7 0.007896488 99.33443884
250 253 6 0.006768419 99.34120726
251 254 6 0.006768419 99.34797568
252 255 5 0.005640349 99.35361603
253 256 8 0.009024558 99.36264059
254 257 10 0.011280698 99.37392128
255 258 11 0.012408767 99.38633005
256 259 11 0.012408767 99.39873882
257 260 3 0.003384209 99.40212303
258 261 7 0.007896488 99.41001952
259 262 11 0.012408767 99.42242828
260 263 11 0.012408767 99.43483705
261 264 13 0.014664907 99.44950196
262 265 8 0.009024558 99.45852652
263 266 6 0.006768419 99.46529493
264 267 5 0.005640349 99.47093528
265 268 13 0.014664907 99.48560019
266 269 4 0.004512279 99.49011247
267 270 7 0.007896488 99.49800896
268 271 11 0.012408767 99.51041772
269 272 9 0.010152628 99.52057035
270 273 9 0.010152628 99.53072298
271 274 12 0.013536837 99.54425982
272 275 9 0.010152628 99.55441244
273 276 10 0.011280698 99.56569314
274 277 8 0.009024558 99.57471770
275 278 7 0.007896488 99.58261419
276 279 18 0.020305256 99.60291944
277 280 10 0.011280698 99.61420014
278 281 8 0.009024558 99.62322470
279 282 7 0.007896488 99.63112119
280 283 11 0.012408767 99.64352996
281 284 9 0.010152628 99.65368258
282 285 8 0.009024558 99.66270714
283 286 4 0.004512279 99.66721942
284 287 4 0.004512279 99.67173170
285 288 3 0.003384209 99.67511591
286 289 6 0.006768419 99.68188433
287 290 3 0.003384209 99.68526854
288 291 2 0.002256140 99.68752468
289 292 3 0.003384209 99.69090889
290 293 5 0.005640349 99.69654923
291 294 4 0.004512279 99.70106151
292 295 4 0.004512279 99.70557379
293 296 3 0.003384209 99.70895800
294 297 5 0.005640349 99.71459835
295 298 1 0.001128070 99.71572642
296 299 6 0.006768419 99.72249484
297 300 2 0.002256140 99.72475098
298 301 2 0.002256140 99.72700712
299 302 1 0.001128070 99.72813519
300 303 1 0.001128070 99.72926326
301 304 6 0.006768419 99.73603168
302 305 5 0.005640349 99.74167202
303 306 5 0.005640349 99.74731237
304 307 4 0.004512279 99.75182465
305 308 2 0.002256140 99.75408079
306 309 1 0.001128070 99.75520886
307 311 2 0.002256140 99.75746500
308 314 4 0.004512279 99.76197728
309 315 2 0.002256140 99.76423342
310 316 4 0.004512279 99.76874570
311 317 2 0.002256140 99.77100184
312 318 2 0.002256140 99.77325798
313 319 2 0.002256140 99.77551412
314 320 2 0.002256140 99.77777026
315 321 1 0.001128070 99.77889833
316 322 3 0.003384209 99.78228254
317 323 1 0.001128070 99.78341061
318 324 2 0.002256140 99.78566675
319 325 1 0.001128070 99.78679482
320 326 2 0.002256140 99.78905095
321 327 3 0.003384209 99.79243516
322 328 4 0.004512279 99.79694744
323 329 2 0.002256140 99.79920358
324 330 4 0.004512279 99.80371586
325 331 1 0.001128070 99.80484393
326 332 2 0.002256140 99.80710007
327 333 1 0.001128070 99.80822814
328 334 1 0.001128070 99.80935621
329 335 1 0.001128070 99.81048428
330 336 2 0.002256140 99.81274042
331 337 2 0.002256140 99.81499656
332 338 1 0.001128070 99.81612463
333 339 2 0.002256140 99.81838077
334 340 1 0.001128070 99.81950884
335 341 3 0.003384209 99.82289305
336 343 2 0.002256140 99.82514919
337 344 1 0.001128070 99.82627726
338 346 2 0.002256140 99.82853340
339 347 2 0.002256140 99.83078954
340 348 1 0.001128070 99.83191761
341 349 4 0.004512279 99.83642988
342 351 2 0.002256140 99.83868602
343 352 1 0.001128070 99.83981409
344 353 1 0.001128070 99.84094216
345 354 1 0.001128070 99.84207023
346 355 2 0.002256140 99.84432637
347 356 2 0.002256140 99.84658251
348 358 1 0.001128070 99.84771058
349 359 2 0.002256140 99.84996672
350 362 1 0.001128070 99.85109479
351 364 1 0.001128070 99.85222286
352 365 1 0.001128070 99.85335093
353 366 2 0.002256140 99.85560707
354 368 1 0.001128070 99.85673514
355 369 2 0.002256140 99.85899128
356 370 2 0.002256140 99.86124742
357 371 1 0.001128070 99.86237549
358 372 2 0.002256140 99.86463163
359 373 2 0.002256140 99.86688777
360 374 1 0.001128070 99.86801584
361 376 1 0.001128070 99.86914391
362 377 2 0.002256140 99.87140005
363 378 2 0.002256140 99.87365619
364 380 1 0.001128070 99.87478426
365 383 3 0.003384209 99.87816847
366 384 2 0.002256140 99.88042461
367 386 1 0.001128070 99.88155268
368 390 1 0.001128070 99.88268074
369 392 1 0.001128070 99.88380881
370 397 1 0.001128070 99.88493688
371 399 1 0.001128070 99.88606495
372 400 3 0.003384209 99.88944916
373 402 1 0.001128070 99.89057723
374 406 2 0.002256140 99.89283337
375 412 3 0.003384209 99.89621758
376 413 1 0.001128070 99.89734565
377 418 1 0.001128070 99.89847372
378 420 1 0.001128070 99.89960179
379 430 3 0.003384209 99.90298600
380 432 1 0.001128070 99.90411407
381 434 1 0.001128070 99.90524214
382 437 1 0.001128070 99.90637021
383 439 1 0.001128070 99.90749828
384 441 2 0.002256140 99.90975442
385 442 1 0.001128070 99.91088249
386 449 1 0.001128070 99.91201056
387 461 2 0.002256140 99.91426670
388 463 1 0.001128070 99.91539477
389 468 1 0.001128070 99.91652284
390 473 1 0.001128070 99.91765091
391 475 1 0.001128070 99.91877898
392 476 1 0.001128070 99.91990705
393 477 1 0.001128070 99.92103512
394 478 1 0.001128070 99.92216319
395 481 1 0.001128070 99.92329126
396 484 1 0.001128070 99.92441933
397 489 1 0.001128070 99.92554740
398 494 1 0.001128070 99.92667547
399 496 1 0.001128070 99.92780354
400 515 1 0.001128070 99.92893161
401 519 1 0.001128070 99.93005967
402 523 1 0.001128070 99.93118774
403 531 3 0.003384209 99.93457195
404 532 1 0.001128070 99.93570002
405 538 1 0.001128070 99.93682809
406 541 1 0.001128070 99.93795616
407 546 1 0.001128070 99.93908423
408 547 2 0.002256140 99.94134037
409 554 1 0.001128070 99.94246844
410 556 1 0.001128070 99.94359651
411 557 2 0.002256140 99.94585265
412 558 1 0.001128070 99.94698072
413 559 1 0.001128070 99.94810879
414 560 1 0.001128070 99.94923686
415 583 1 0.001128070 99.95036493
416 591 1 0.001128070 99.95149300
417 594 1 0.001128070 99.95262107
418 595 1 0.001128070 99.95374914
419 598 1 0.001128070 99.95487721
420 602 1 0.001128070 99.95600528
421 609 1 0.001128070 99.95713335
422 613 2 0.002256140 99.95938949
423 624 1 0.001128070 99.96051756
424 640 2 0.002256140 99.96277370
425 646 1 0.001128070 99.96390177
426 651 1 0.001128070 99.96502984
427 657 2 0.002256140 99.96728598
428 677 1 0.001128070 99.96841405
429 694 1 0.001128070 99.96954212
430 714 2 0.002256140 99.97179826
431 717 1 0.001128070 99.97292633
432 720 1 0.001128070 99.97405440
433 723 1 0.001128070 99.97518247
434 726 2 0.002256140 99.97743860
435 727 2 0.002256140 99.97969474
436 737 1 0.001128070 99.98082281
437 763 1 0.001128070 99.98195088
438 818 1 0.001128070 99.98307895
439 838 1 0.001128070 99.98420702
440 841 1 0.001128070 99.98533509
441 853 1 0.001128070 99.98646316
442 863 1 0.001128070 99.98759123
443 909 1 0.001128070 99.98871930
444 915 1 0.001128070 99.98984737
445 929 1 0.001128070 99.99097544
446 939 1 0.001128070 99.99210351
447 973 1 0.001128070 99.99323158
448 1098 1 0.001128070 99.99435965
449 1221 1 0.001128070 99.99548772
450 1286 1 0.001128070 99.99661579
451 1307 1 0.001128070 99.99774386
452 1962 1 0.001128070 99.99887193
453 4165 1 0.001128070 100.00000000
# A tibble: 606 × 4
# Groups: length_url [453]
length_url phishing n porc
<int> <fct> <int> <dbl>
1 4 0 2 100
2 5 0 19 90.5
3 5 1 2 9.52
4 6 0 73 100
5 7 0 169 96.6
6 7 1 6 3.43
7 8 0 273 95.1
8 8 1 14 4.88
9 9 0 446 93.1
10 9 1 33 6.89
# … with 596 more rows
# Visualización
ggplot(phishing_bruto, aes(x = length_url)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por longitud de la URL",
x = "Longitud de la URL", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_x_continuous(breaks = seq(0, 4000, 500)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Vemos por su distribución que se trata de una variable con una distribución considerablemente asimétrica, con una cola pesada a la derecha. Por otra parte, a partir de un valor de 500, apenas podemos visualizar registros, y parece que todos ellos se tratan de páginas web de phishing:
phishing n porc
1 0 1 1.5625
2 1 63 98.4375
min max
1 4 4165
No tiene sentido tener un número tan elevado de longitud de URL, por lo que teniendo en cuenta la distribución y las proporciones a partir del valor 500, podemos imputar el 500 a todo valor que sea superior a este.
# Anañlisis cuantitativo
phishing_bruto |> count(email_in_url) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
email_in_url n porc cumul
1 0 87022 98.166887 98.16689
2 1 1625 1.833113 100.00000
# A tibble: 4 × 4
# Groups: email_in_url [2]
email_in_url phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57999 66.6
2 0 1 29023 33.4
3 1 0 1 0.0615
4 1 1 1624 99.9
# Visualización
ggplot(phishing_bruto, aes(x = email_in_url)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por email en la URL",
x = "Email en URL", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Tenemos una variable tipo binaria que nos indica con el valor 0 que no existe un email en la URL, y con un 1 que sí existe. Vemos que la mayoría de datos se concentran en el 0, donde la distribución de la objetivo es similar a la global. En este caso la información relevante para nuestros futuros modelos la encontramos en cuando sí existe email en la URL, ya que el porcentaje de encontrarnos una web con phishing es de casi el 100%. Por lo tanto, no haremos ninguna modificación en esta variable. En este caso consideramos que un porcentaje de datos en el nivel diferenciador cercano al 2%, sí puede llegar a ser determinante a la hora de ayudar a nuestro modelo a mejorar la clasificación.
# Anañlisis cuantitativo
phishing_bruto |> count(url_google_index) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
url_google_index n porc cumul
1 -1 90 0.1015263 0.1015263
2 0 88342 99.6559387 99.7574650
3 1 215 0.2425350 100.0000000
# A tibble: 5 × 4
# Groups: url_google_index [3]
url_google_index phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 90 100
2 0 0 57716 65.3
3 0 1 30626 34.7
4 1 0 194 90.2
5 1 1 21 9.77
# Visualización
ggplot(phishing_bruto, aes(x = url_google_index)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por URL indexada en Google",
x = "URL Indexada en Google", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Una vez más, apenas tenemos registros en los niveles distintos de 0, donde las proporciones son idénticas a las globales en función de nuestra objetivo. Por lo tanto, y a pesar de que si tenemos proporciones diferenciales en los demás niveles, eliminaremos esta variable por que en dichos niveles los registros apenas llegan al 0,5%, y para evitar el sobreajuste.
# Anañlisis cuantitativo
phishing_bruto |> count(url_shortened) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
url_shortened n porc cumul
1 0 88161 99.4517581 99.45176
2 1 486 0.5482419 100.00000
# A tibble: 4 × 4
# Groups: url_shortened [2]
url_shortened phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57991 65.8
2 0 1 30170 34.2
3 1 0 9 1.85
4 1 1 477 98.1
# Visualización
ggplot(phishing_bruto, aes(x = url_shortened)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por URL acortada",
x = "URL acortada", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Sucede lo mismo que con la variable de email en la URL. Cuando sí esta acortada, hay una mayor probabilidad de que la web sea de phishing, pero apenas tenemos un 1% del total de datos en este nivel. De esta forma, convendría eliminar esta variable, ya que la probabilidad de que nos pueda aportar información relevante para nuestro modelo es muy baja.
Continuando con la estructura anterior, pasaremos a analizar las variables de tipo dominio:
qty_dot_domain: cantidad de (.) en dominio qty_hyphen_domain: cantidad de (-) en dominio qty_underline_domain: cantidad de (_) en dominio qty_slash_domain: cantidad de (/) en dominio qty_questionmark_domain: cantidad de (?) en dominio qty_equal_domain: cantidad de (=) en dominio qty_at_domain: cantidad de (@) en dominio qty_and_domain: cantidad de (&) en dominio qty_exclamation_domain: cantidad de (!) en dominio qty_space_domain: cantidad de ( ) en dominio qty_tilde_domain: cantidad de (~) en dominio qty_comma_domain: cantidad de (,) en dominio qty_plus_domain: cantidad de (+) en dominio qty_asterisk_domain: cantidad de (*) en dominio qty_hashtag_domain: cantidad de (#) en dominio qty_dollar_domain: cantidad de ($) en dominio qty_percent_domain: cantidad de (%) en dominio qty_vowels_domain: cantidad de vocales en dominio domain_length: longitud de dominio domain_in_ip: dominio en formato de dirección IP server_client_domain: dominio contiene las palabras clave “cliente” o “servidor” domain_spf: dominio tiene SPF time_domain_activation: tiempo (en dias) de la activación del dominio time_domain_expiration: tiempo (en dias) de la expiración del dominio
Comprobamos el número de valores únicos en estas variables:
phishing_bruto |> select(contains("domain")) |>
summarise_all(list(~n_distinct(.)))
qty_dot_domain qty_hyphen_domain qty_underline_domain
1 19 12 4
qty_slash_domain qty_questionmark_domain qty_equal_domain
1 1 1 1
qty_at_domain qty_and_domain qty_exclamation_domain
1 2 1 1
qty_space_domain qty_tilde_domain qty_comma_domain qty_plus_domain
1 1 1 1 1
qty_asterisk_domain qty_hashtag_domain qty_dollar_domain
1 1 1 1
qty_percent_domain qty_vowels_domain domain_length domain_in_ip
1 1 42 94 2
server_client_domain domain_spf time_domain_activation
1 2 3 8790
time_domain_expiration domain_google_index
1 3093 3
Convertimos las variables a factor para una mejor visualización:
phishing_bruto <-
phishing_bruto |> mutate(qty_dot_domain = as_factor(qty_dot_domain),
qty_hyphen_domain = as_factor(qty_hyphen_domain),
qty_underline_domain = as_factor(qty_underline_domain),
qty_slash_domain = as_factor(qty_slash_domain),
qty_questionmark_domain = as_factor(qty_questionmark_domain),
qty_equal_domain = as_factor(qty_equal_domain),
qty_at_domain = as_factor(qty_at_domain),
qty_and_domain = as_factor(qty_and_domain),
qty_exclamation_domain = as_factor(qty_exclamation_domain),
qty_space_domain = as_factor(qty_space_domain),
qty_tilde_domain = as_factor(qty_tilde_domain),
qty_comma_domain = as_factor(qty_comma_domain),
qty_plus_domain = as_factor(qty_plus_domain),
qty_asterisk_domain = as_factor(qty_asterisk_domain),
qty_hashtag_domain = as_factor(qty_hashtag_domain),
qty_dollar_domain = as_factor(qty_dollar_domain),
qty_percent_domain = as_factor(qty_percent_domain),
domain_in_ip = as_factor(domain_in_ip),
server_client_domain = as_factor(server_client_domain),
domain_spf = as_factor(domain_spf))
Una vez más, evitamos convertir en factor variables con una gran cantidad de valores únicos, en este caso, las variables qty_vowels_domain, domain_length, time_domain_activation y time_domain_expiration.
Análisis cuantitativo
# qty_dot_domain
phishing_bruto |> group_by(qty_dot_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 26 × 4
# Groups: qty_dot_domain [19]
qty_dot_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 14 11.3
2 0 1 110 88.7
3 1 0 8562 36.8
4 1 1 14707 63.2
5 2 0 42096 75.5
6 2 1 13647 24.5
7 3 0 5868 76.6
8 3 1 1796 23.4
9 4 0 1415 87.0
10 4 1 211 13.0
# … with 16 more rows
# qty_hyphen_domain
phishing_bruto |> group_by(qty_hyphen_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 19 × 4
# Groups: qty_hyphen_domain [12]
qty_hyphen_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 54067 67.1
2 0 1 26503 32.9
3 1 0 3410 50.8
4 1 1 3303 49.2
5 2 0 450 46.6
6 2 1 516 53.4
7 3 0 57 26.1
8 3 1 161 73.9
9 4 0 10 9.35
10 4 1 97 90.7
11 5 0 1 2.5
12 5 1 39 97.5
13 6 0 5 22.7
14 6 1 17 77.3
15 7 1 4 100
16 8 1 1 100
17 9 1 1 100
18 10 1 2 100
19 11 1 3 100
# qty_underline_domain
phishing_bruto |> group_by(qty_underline_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 7 × 4
# Groups: qty_underline_domain [4]
qty_underline_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57959 65.4
2 0 1 30633 34.6
3 1 0 33 75
4 1 1 11 25
5 2 0 7 70
6 2 1 3 30
7 3 0 1 100
# qty_slash_domain
phishing_bruto |> group_by(qty_slash_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_slash_domain [1]
qty_slash_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_questionmark_domain
phishing_bruto |> group_by(qty_questionmark_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_questionmark_domain [1]
qty_questionmark_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_equal_domain
phishing_bruto |> group_by(qty_equal_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_equal_domain [1]
qty_equal_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
Visualización:
# qty_dot_domain
ggplot(phishing_bruto, aes(x = qty_dot_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (.) en dominio",
x = "Cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hyphen_domain
ggplot(phishing_bruto, aes(x = qty_hyphen_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (-) en dominio",
x = "Cantidad de (-)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_underline_domain
ggplot(phishing_bruto, aes(x = qty_underline_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (_) en dominio",
x = "Cantidad de (_)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# # qty_slash_domain
ggplot(phishing_bruto, aes(x = qty_slash_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (/) en dominio",
x = "Cantidad de (/)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_questionmark_domain
ggplot(phishing_bruto, aes(x = qty_questionmark_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (?) en dominio",
x = "Cantidad de (?)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_equal_domain
ggplot(phishing_bruto, aes(x = qty_equal_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (=) en dominio",
x = "Cantidad de (=)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conlcusiones:
qty_dot_domain: Cuando el número de punto es 0, o se encuentra comprendido entre el 6 y el 12,
la probabilidad de econtrarse con una web de phishing es del 90% o superior. Cuando tenemos 2, 3 o 4
puntos, existe una mayor proporción de webs legítimas. y por último, cuando tenemos 1 o 5 puntos,
tenemos un porcentaje de phishing superior a los porcentajes globales, aunque considerablemente inferior
si lo comparamos con los niveles 0, 6-12. Por lo tanto podemos generar 3 niveles a partir de los valores
que puede tomar esta variable: 0/6-12, 2-4, 1/5qty_hyphen_domain: Podemos crear 3 niveles nuevamente. Cuando tenemos 0 guiones, las proporciones
son muy similares a las globales. Cuando es 1 o 2, el porcentaje de phishing asciende al 50% aproximadamente
y cuando tenemos 3 o más guiones, siempre se encuentra este porcentaje por encima del 70%, teniendo
niveles con un 100% de phishing: 0, 1-2, >2.qty_underline_domain: La gran mayoría de datos se concentran en el nivel 0, y cuando toma diferentes
valores, no encontramos información muy relevante acerca de la proporción de phishing: la eliminamos.qty_slash_domain: Solo toma un nivel esta variable, y a parte, su distribución con la objetivo
tampoco nos aporta información relevante para detectar una posible web de phishing: la eliminamos.qty_questionmark_domain: Sucede lo mismo que con la anterior variable: la eliminamos.qty_equal_domain: Nuevamente la eliminamos por el mismo motivo.Análisis cuantitativo:
# qty_at_domain
phishing_bruto |> group_by(qty_at_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 3 × 4
# Groups: qty_at_domain [2]
qty_at_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30646 34.6
3 1 1 1 100
# qty_and_domain
phishing_bruto |> group_by(qty_and_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_and_domain [1]
qty_and_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_exclamation_domain
phishing_bruto |> group_by(qty_exclamation_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_exclamation_domain [1]
qty_exclamation_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_space_domain
phishing_bruto |> group_by(qty_space_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_space_domain [1]
qty_space_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_tilde_domain
phishing_bruto |> group_by(qty_tilde_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_tilde_domain [1]
qty_tilde_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_comma_domain
phishing_bruto |> group_by(qty_comma_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_comma_domain [1]
qty_comma_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
Ya viendo como se distribuyen estas variables con la objetivo, no pasaremos a analizarlas visualmente. Es decir, vemos que únicamente toman un nivel, excepto en el caso de qty_at_domain, donde solo tiene un registro en el otro. Las proporciones con la objetivo son idénticas a las globales por lo que no nos aporta una información diferencial para ayudar a nuestro modelo a detectar el phishing. De este modo, las eliminaremos de nuestro análisis.
Análisis cuantitativo:
# qty_plus_domain
phishing_bruto |> group_by(qty_plus_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_plus_domain [1]
qty_plus_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_asterisk_domain
phishing_bruto |> group_by(qty_asterisk_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_asterisk_domain [1]
qty_asterisk_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_hashtag_domain
phishing_bruto |> group_by(qty_hashtag_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_hashtag_domain [1]
qty_hashtag_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_dollar_domain
phishing_bruto |> group_by(qty_dollar_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_dollar_domain [1]
qty_dollar_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_percent_domain
phishing_bruto |> group_by(qty_percent_domain) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 2 × 4
# Groups: qty_percent_domain [1]
qty_percent_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 58000 65.4
2 0 1 30647 34.6
# qty_vowels_domain
phishing_bruto |> count(qty_vowels_domain) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_vowels_domain n porc cumul
1 0 672 0.758062879 0.7580629
2 1 2749 3.101063770 3.8591266
3 2 4748 5.356075220 9.2152019
4 3 9872 11.136304669 20.3515065
5 4 14628 16.501404447 36.8529110
6 5 16331 18.422507248 55.2754182
7 6 14632 16.505916726 71.7813350
8 7 9588 10.815932857 82.5972678
9 8 6322 7.131657022 89.7289248
10 9 4134 4.663440387 94.3923652
11 10 2116 2.386995612 96.7793608
12 11 1294 1.459722269 98.2390831
13 12 587 0.662176949 98.9012601
14 13 357 0.402720904 99.3039810
15 14 237 0.267352533 99.5713335
16 15 98 0.110550836 99.6818843
17 16 62 0.069940325 99.7518247
18 17 41 0.046250860 99.7980755
19 18 26 0.029329814 99.8274053
20 19 35 0.039482442 99.8668878
21 20 47 0.053019279 99.9199070
22 21 11 0.012408767 99.9323158
23 22 20 0.022561395 99.9548772
24 23 6 0.006768419 99.9616456
25 24 3 0.003384209 99.9650298
26 25 2 0.002256140 99.9672860
27 26 3 0.003384209 99.9706702
28 27 2 0.002256140 99.9729263
29 28 5 0.005640349 99.9785667
30 29 1 0.001128070 99.9796947
31 31 2 0.002256140 99.9819509
32 33 2 0.002256140 99.9842070
33 35 5 0.005640349 99.9898474
34 36 1 0.001128070 99.9909754
35 38 1 0.001128070 99.9921035
36 39 1 0.001128070 99.9932316
37 41 1 0.001128070 99.9943597
38 44 1 0.001128070 99.9954877
39 48 1 0.001128070 99.9966158
40 50 1 0.001128070 99.9977439
41 53 1 0.001128070 99.9988719
42 61 1 0.001128070 100.0000000
Con las 5 primeras de nuestro último grupo de variables tipo qty_domain, sucede lo mismo con las anteriores. Por lo tanto, eliminaremos las variables qty_plus/asterisk/hashtag/dollar/percent_domain.
Visualizamos la última de ellas:
# qty_vowels_domain
ggplot(phishing_bruto, aes(x = qty_vowels_domain)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de vocales en dominio",
x = "Cantidad de vocales", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 30000, 5000)) +
scale_x_continuous(breaks = seq(0, 60 ,10)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_vowels_domain: Vemos que es una variable con una distribución
considerablemente asimétrica, donde la mayoría registros se concentrar entre
los valores 2 y 9 (> 65% de proporción sobre el total). A partir del valor
30 aproximadamente prácticamente no se distinguen registros: phishing n porc
1 0 1 1.408451
2 1 70 98.591549
Solo encontramos 71 registros donde únicamente tenemos 1/71 webs legítimas. Por lo tanto, podríamos imputarle el valor 20 a todo registro que supere este límite en función de esta variable y de esta forma simplificamos el rango que abarca.
Esta variable indica la longitud del dominio en caracteres:
domain_length n porc cumul
1 4 97 0.109422767 0.1094228
2 5 156 0.175978883 0.2854016
3 6 586 0.661048879 0.9464505
4 7 599 0.675713786 1.6221643
5 8 719 0.811082157 2.4332465
6 9 995 1.122429411 3.5556759
7 10 1816 2.048574684 5.6042506
8 11 3173 3.579365348 9.1836159
9 12 4207 4.745789480 13.9294054
10 13 4445 5.014270082 18.9436755
11 14 5531 6.239353842 25.1830293
12 15 5904 6.660123862 31.8431532
13 16 8218 9.270477286 41.1136305
14 17 6195 6.988392162 48.1020226
15 18 6214 7.009825488 55.1118481
16 19 6003 6.771802768 61.8836509
17 20 5596 6.312678376 68.1963293
18 21 4864 5.486931312 73.6832606
19 22 4249 4.793168410 78.4764290
20 23 3510 3.959524857 82.4359538
21 24 2858 3.224023374 85.6599772
22 25 2496 2.815662121 88.4756393
23 26 1943 2.191839543 90.6674789
24 27 1679 1.894029127 92.5615080
25 28 1723 1.943664196 94.5051722
26 29 1017 1.147246946 95.6524191
27 30 749 0.844924250 96.4973434
28 31 607 0.684738344 97.1820817
29 32 440 0.496350694 97.6784324
30 33 397 0.447843695 98.1262761
31 34 283 0.319243742 98.4455199
32 35 234 0.263968324 98.7094882
33 36 181 0.204180627 98.9136688
34 37 152 0.171466603 99.0851354
35 38 133 0.150033278 99.2351687
36 39 96 0.108294697 99.3434634
37 40 62 0.069940325 99.4134037
38 41 51 0.057531558 99.4709353
39 42 57 0.064299976 99.5352353
40 43 48 0.054147348 99.5893826
41 44 35 0.039482442 99.6288650
42 45 27 0.030457884 99.6593229
43 46 11 0.012408767 99.6717317
44 47 24 0.027073674 99.6988054
45 48 15 0.016921046 99.7157264
46 49 17 0.019177186 99.7349036
47 50 24 0.027073674 99.7619773
48 51 8 0.009024558 99.7710018
49 52 7 0.007896488 99.7788983
50 53 8 0.009024558 99.7879229
51 54 6 0.006768419 99.7946913
52 55 5 0.005640349 99.8003317
53 56 10 0.011280698 99.8116124
54 57 10 0.011280698 99.8228930
55 58 6 0.006768419 99.8296615
56 59 2 0.002256140 99.8319176
57 60 9 0.010152628 99.8420702
58 61 3 0.003384209 99.8454544
59 62 29 0.032714023 99.8781685
60 63 5 0.005640349 99.8838088
61 64 9 0.010152628 99.8939614
62 65 2 0.002256140 99.8962176
63 66 3 0.003384209 99.8996018
64 67 4 0.004512279 99.9041141
65 68 10 0.011280698 99.9153948
66 69 1 0.001128070 99.9165228
67 70 4 0.004512279 99.9210351
68 71 5 0.005640349 99.9266755
69 72 5 0.005640349 99.9323158
70 73 4 0.004512279 99.9368281
71 74 4 0.004512279 99.9413404
72 75 3 0.003384209 99.9447246
73 77 5 0.005640349 99.9503649
74 83 2 0.002256140 99.9526211
75 84 4 0.004512279 99.9571333
76 87 4 0.004512279 99.9616456
77 89 5 0.005640349 99.9672860
78 90 1 0.001128070 99.9684140
79 92 2 0.002256140 99.9706702
80 93 1 0.001128070 99.9717983
81 99 5 0.005640349 99.9774386
82 113 1 0.001128070 99.9785667
83 117 4 0.004512279 99.9830790
84 126 2 0.002256140 99.9853351
85 128 1 0.001128070 99.9864632
86 132 1 0.001128070 99.9875912
87 137 1 0.001128070 99.9887193
88 142 1 0.001128070 99.9898474
89 145 1 0.001128070 99.9909754
90 150 1 0.001128070 99.9921035
91 161 1 0.001128070 99.9932316
92 173 1 0.001128070 99.9943597
93 179 3 0.003384209 99.9977439
94 231 2 0.002256140 100.0000000
ggplot(phishing_bruto, aes(x = domain_length)) +
geom_density(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por longitud de dominio",
x = "Longitud de dominio", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_x_continuous(breaks = seq(0, 250, 25)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Vemos que se trata de una variable con una distribución muy asimétrica, con una cola pesada a la derecha. La mayoría de datos se concentran entre los valores 13 y 21 aproximadamente.
phishing_bruto |> select(domain_length) |>
summarise(min = min(domain_length), max = max(domain_length))
min max
1 4 231
Tenemso un máximo de longitud de dominio muy elevado. Observando el gráfico de densidad, vemos que a partir del valor 50, a parte de tener muy pocos datos, casi la totalidad de registros de corresponden a webs de phishing. Teniendo todo ello en cuenta, podríamos imputarle a todo lo que supere el 50, dicho valor.
Indica si el dominio está en formato de dirección ip
# Anañlisis cuantitativo
phishing_bruto |> count(domain_in_ip) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
domain_in_ip n porc cumul
1 0 88446 99.773258 99.77326
2 1 201 0.226742 100.00000
# A tibble: 4 × 4
# Groups: domain_in_ip [2]
domain_in_ip phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57985 65.6
2 0 1 30461 34.4
3 1 0 15 7.46
4 1 1 186 92.5
# Visualización
ggplot(phishing_bruto, aes(x = domain_in_ip)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por formato ip en dominio",
x = "Formato ip en dominio", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Eliminaremos esta variable, ya que unicamente tenemos un 0.22% del total de datos en aquel nivel que parece aportar información relevante para nuestra clasifiación. Casi el 100% de datos se concentra en el 0, donde las proporciones son idénticas a las globales.
Indica si el dominio contiene las palabras clave “cliente” o “servidor”.
# Anañlisis cuantitativo
phishing_bruto |> count(server_client_domain) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
server_client_domain n porc cumul
1 0 88248 99.5499002 99.5499
2 1 399 0.4500998 100.0000
# A tibble: 4 × 4
# Groups: server_client_domain [2]
server_client_domain phishing n porc
<fct> <fct> <int> <dbl>
1 0 0 57747 65.4
2 0 1 30501 34.6
3 1 0 253 63.4
4 1 1 146 36.6
# Visualización
ggplot(phishing_bruto, aes(x = server_client_domain)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por palabras clave cliente o servidor en dominio",
x = "Palabras clave cliente o servidor en dominio", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
En este caso, aparte de que apenas tenemos el 0,5% del total de registros en uno de los dos niveles de esta variable, sus proporciones no reflejan información diferenciadora si la comparamos con las proporciones globales. Por otro lado, en el nivel modal, sucede lo mismo que con la variable anterior, por lo que eliminaremos esta variable del modelo.
Indica si el dominio tiene SPF.
# Anañlisis cuantitativo
phishing_bruto |> count(domain_spf) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
domain_spf n porc cumul
1 -1 14677 16.55668 16.55668
2 0 60882 68.67914 85.23582
3 1 13088 14.76418 100.00000
# A tibble: 6 × 4
# Groups: domain_spf [3]
domain_spf phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 7780 53.0
2 -1 1 6897 47.0
3 0 0 43348 71.2
4 0 1 17534 28.8
5 1 0 6872 52.5
6 1 1 6216 47.5
# Visualización
ggplot(phishing_bruto, aes(x = domain_spf)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por SPF en dominio",
x = "SPF en dominio", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Cuando toma el valor 1 o -1, el porcentaje de phishing es superior. En su valor más frecuente (0), la proporción de legítimas es más elevada. Por lo tanto, podemos reagrupar los niveles 1 y -1 en otro que sea distinto de 0: 0, ≠0.
# Anañlisis cuantitativo
phishing_bruto |> count(time_domain_activation) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
time_domain_activation n porc cumul
1 -1 24354 27.473010931 27.47301
2 1 1 0.001128070 27.47414
3 2 6 0.006768419 27.48091
4 3 16 0.018049116 27.49896
5 4 22 0.024817535 27.52377
6 5 29 0.032714023 27.55649
7 6 19 0.021433325 27.57792
8 7 28 0.031585953 27.60951
9 8 47 0.053019279 27.66253
10 9 40 0.045122790 27.70765
11 10 28 0.031585953 27.73924
12 11 42 0.047378930 27.78661
13 12 35 0.039482442 27.82610
14 13 42 0.047378930 27.87348
15 14 38 0.042866651 27.91634
16 15 40 0.045122790 27.96147
17 16 38 0.042866651 28.00433
18 17 19 0.021433325 28.02577
19 18 19 0.021433325 28.04720
20 19 21 0.023689465 28.07089
21 20 24 0.027073674 28.09796
22 21 39 0.043994721 28.14196
23 22 32 0.036098232 28.17805
24 23 20 0.022561395 28.20062
25 24 16 0.018049116 28.21867
26 25 9 0.010152628 28.22882
27 26 18 0.020305256 28.24912
28 27 16 0.018049116 28.26717
29 28 28 0.031585953 28.29876
30 29 27 0.030457884 28.32922
31 30 10 0.011280698 28.34050
32 31 36 0.040610511 28.38111
33 32 13 0.014664907 28.39577
34 33 24 0.027073674 28.42285
35 34 22 0.024817535 28.44766
36 35 8 0.009024558 28.45669
37 36 24 0.027073674 28.48376
38 37 18 0.020305256 28.50407
39 38 21 0.023689465 28.52776
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1594 1628 6 0.006768419 40.67256
1595 1630 12 0.013536837 40.68609
1596 1631 2 0.002256140 40.68835
1597 1632 2 0.002256140 40.69060
1598 1633 2 0.002256140 40.69286
1599 1634 32 0.036098232 40.72896
1600 1635 16 0.018049116 40.74701
1601 1636 7 0.007896488 40.75490
1602 1637 7 0.007896488 40.76280
1603 1638 4 0.004512279 40.76731
1604 1639 23 0.025945604 40.79326
1605 1640 11 0.012408767 40.80567
1606 1641 16 0.018049116 40.82372
1607 1642 8 0.009024558 40.83274
1608 1643 4 0.004512279 40.83725
1609 1644 5 0.005640349 40.84289
1610 1645 4 0.004512279 40.84741
1611 1646 6 0.006768419 40.85417
1612 1647 8 0.009024558 40.86320
1613 1648 5 0.005640349 40.86884
1614 1649 8 0.009024558 40.87786
1615 1650 6 0.006768419 40.88463
1616 1651 5 0.005640349 40.89027
1617 1652 2 0.002256140 40.89253
1618 1653 4 0.004512279 40.89704
1619 1654 31 0.034970163 40.93201
1620 1655 7 0.007896488 40.93991
1621 1656 4 0.004512279 40.94442
1622 1657 12 0.013536837 40.95796
1623 1658 8 0.009024558 40.96698
1624 1659 2 0.002256140 40.96924
1625 1660 7 0.007896488 40.97713
1626 1661 3 0.003384209 40.98052
1627 1662 3 0.003384209 40.98390
1628 1663 10 0.011280698 40.99518
1629 1664 2 0.002256140 40.99744
1630 1665 5 0.005640349 41.00308
1631 1666 5 0.005640349 41.00872
1632 1667 1 0.001128070 41.00985
1633 1668 9 0.010152628 41.02000
1634 1669 5 0.005640349 41.02564
1635 1670 5 0.005640349 41.03128
1636 1671 13 0.014664907 41.04595
1637 1672 4 0.004512279 41.05046
1638 1673 7 0.007896488 41.05836
1639 1674 7 0.007896488 41.06625
1640 1675 8 0.009024558 41.07528
1641 1676 6 0.006768419 41.08204
1642 1677 6 0.006768419 41.08881
1643 1678 10 0.011280698 41.10009
1644 1679 3 0.003384209 41.10348
1645 1680 2 0.002256140 41.10573
1646 1681 4 0.004512279 41.11025
1647 1682 4 0.004512279 41.11476
1648 1683 3 0.003384209 41.11814
1649 1684 10 0.011280698 41.12942
1650 1685 9 0.010152628 41.13958
1651 1686 6 0.006768419 41.14634
1652 1687 12 0.013536837 41.15988
1653 1688 9 0.010152628 41.17003
1654 1689 3 0.003384209 41.17342
1655 1690 6 0.006768419 41.18019
1656 1691 10 0.011280698 41.19147
1657 1692 1 0.001128070 41.19260
1658 1693 8 0.009024558 41.20162
1659 1694 18 0.020305256 41.22193
1660 1696 2 0.002256140 41.22418
1661 1697 2 0.002256140 41.22644
1662 1698 5 0.005640349 41.23208
1663 1699 11 0.012408767 41.24449
1664 1700 8 0.009024558 41.25351
1665 1701 3 0.003384209 41.25690
1666 1702 8 0.009024558 41.26592
1667 1703 2 0.002256140 41.26818
1668 1704 2 0.002256140 41.27043
1669 1705 4 0.004512279 41.27494
1670 1706 4 0.004512279 41.27946
1671 1707 3 0.003384209 41.28284
1672 1708 8 0.009024558 41.29187
1673 1709 4 0.004512279 41.29638
1674 1710 2 0.002256140 41.29863
1675 1711 2 0.002256140 41.30089
1676 1712 2 0.002256140 41.30315
1677 1713 2 0.002256140 41.30540
1678 1714 1 0.001128070 41.30653
1679 1715 8 0.009024558 41.31555
1680 1716 6 0.006768419 41.32232
1681 1717 4 0.004512279 41.32684
1682 1718 4 0.004512279 41.33135
1683 1720 8 0.009024558 41.34037
1684 1721 13 0.014664907 41.35504
1685 1722 2 0.002256140 41.35729
1686 1723 4 0.004512279 41.36181
1687 1724 3 0.003384209 41.36519
1688 1725 5 0.005640349 41.37083
1689 1726 5 0.005640349 41.37647
1690 1727 13 0.014664907 41.39114
1691 1728 1 0.001128070 41.39226
1692 1729 1 0.001128070 41.39339
1693 1730 4 0.004512279 41.39790
1694 1731 6 0.006768419 41.40467
1695 1732 7 0.007896488 41.41257
1696 1733 8 0.009024558 41.42159
1697 1734 1 0.001128070 41.42272
1698 1735 4 0.004512279 41.42723
1699 1736 6 0.006768419 41.43400
1700 1737 1 0.001128070 41.43513
1701 1738 5 0.005640349 41.44077
1702 1739 1 0.001128070 41.44190
1703 1740 2 0.002256140 41.44415
1704 1741 2 0.002256140 41.44641
1705 1742 10 0.011280698 41.45769
1706 1743 6 0.006768419 41.46446
1707 1744 4 0.004512279 41.46897
1708 1745 7 0.007896488 41.47687
1709 1746 1 0.001128070 41.47800
1710 1748 2 0.002256140 41.48025
1711 1749 3 0.003384209 41.48364
1712 1750 3 0.003384209 41.48702
1713 1751 4 0.004512279 41.49153
1714 1752 2 0.002256140 41.49379
1715 1753 3 0.003384209 41.49717
1716 1754 6 0.006768419 41.50394
1717 1755 2 0.002256140 41.50620
1718 1756 3 0.003384209 41.50958
1719 1757 18 0.020305256 41.52989
1720 1758 2 0.002256140 41.53214
1721 1759 6 0.006768419 41.53891
1722 1760 7 0.007896488 41.54681
1723 1761 3 0.003384209 41.55019
1724 1762 11 0.012408767 41.56260
1725 1763 7 0.007896488 41.57050
1726 1764 6 0.006768419 41.57727
1727 1765 2 0.002256140 41.57952
1728 1766 2 0.002256140 41.58178
1729 1767 5 0.005640349 41.58742
1730 1768 8 0.009024558 41.59644
1731 1769 12 0.013536837 41.60998
1732 1770 6 0.006768419 41.61675
1733 1772 8 0.009024558 41.62577
1734 1773 6 0.006768419 41.63254
1735 1774 5 0.005640349 41.63818
1736 1775 17 0.019177186 41.65736
1737 1776 6 0.006768419 41.66413
1738 1777 7 0.007896488 41.67202
1739 1778 7 0.007896488 41.67992
1740 1779 4 0.004512279 41.68443
1741 1780 6 0.006768419 41.69120
1742 1781 2 0.002256140 41.69346
1743 1782 5 0.005640349 41.69910
1744 1783 1 0.001128070 41.70023
1745 1784 1 0.001128070 41.70135
1746 1785 1 0.001128070 41.70248
1747 1786 4 0.004512279 41.70700
1748 1787 20 0.022561395 41.72956
1749 1788 8 0.009024558 41.73858
1750 1789 7 0.007896488 41.74648
1751 1790 8 0.009024558 41.75550
1752 1791 3 0.003384209 41.75889
1753 1792 3 0.003384209 41.76227
1754 1793 5 0.005640349 41.76791
1755 1794 7 0.007896488 41.77581
1756 1795 8 0.009024558 41.78483
1757 1796 5 0.005640349 41.79047
1758 1797 9 0.010152628 41.80062
1759 1798 20 0.022561395 41.82319
1760 1799 11 0.012408767 41.83560
1761 1800 2 0.002256140 41.83785
1762 1801 1 0.001128070 41.83898
1763 1802 6 0.006768419 41.84575
1764 1803 6 0.006768419 41.85252
1765 1804 10 0.011280698 41.86380
1766 1805 8 0.009024558 41.87282
1767 1806 4 0.004512279 41.87733
1768 1807 5 0.005640349 41.88297
1769 1808 15 0.016921046 41.89990
1770 1809 10 0.011280698 41.91118
1771 1810 18 0.020305256 41.93148
1772 1811 6 0.006768419 41.93825
1773 1812 2 0.002256140 41.94051
1774 1813 7 0.007896488 41.94840
1775 1814 2 0.002256140 41.95066
1776 1815 1 0.001128070 41.95179
1777 1816 4 0.004512279 41.95630
1778 1817 1 0.001128070 41.95743
1779 1818 7 0.007896488 41.96532
1780 1819 34 0.038354372 42.00368
1781 1820 61 0.068812255 42.07249
1782 1821 5 0.005640349 42.07813
1783 1822 6 0.006768419 42.08490
1784 1823 8 0.009024558 42.09392
1785 1824 8 0.009024558 42.10295
1786 1825 2 0.002256140 42.10520
1787 1826 3 0.003384209 42.10859
1788 1827 6 0.006768419 42.11536
1789 1828 38 0.042866651 42.15822
1790 1829 16 0.018049116 42.17627
1791 1830 4 0.004512279 42.18078
1792 1831 7 0.007896488 42.18868
1793 1832 4 0.004512279 42.19319
1794 1833 8 0.009024558 42.20222
1795 1834 5 0.005640349 42.20786
1796 1835 1 0.001128070 42.20899
1797 1836 5 0.005640349 42.21463
1798 1837 4 0.004512279 42.21914
1799 1838 7 0.007896488 42.22704
1800 1839 17 0.019177186 42.24621
1801 1840 3 0.003384209 42.24960
1802 1841 9 0.010152628 42.25975
1803 1842 9 0.010152628 42.26990
1804 1843 7 0.007896488 42.27780
1805 1844 4 0.004512279 42.28231
1806 1845 5 0.005640349 42.28795
1807 1846 5 0.005640349 42.29359
1808 1847 7 0.007896488 42.30149
1809 1848 8 0.009024558 42.31051
1810 1849 8 0.009024558 42.31954
1811 1850 9 0.010152628 42.32969
1812 1851 5 0.005640349 42.33533
1813 1852 2 0.002256140 42.33759
1814 1853 1 0.001128070 42.33871
1815 1854 4 0.004512279 42.34323
1816 1855 1 0.001128070 42.34435
1817 1856 3 0.003384209 42.34774
1818 1857 1 0.001128070 42.34887
1819 1858 2 0.002256140 42.35112
1820 1859 1 0.001128070 42.35225
1821 1860 6 0.006768419 42.35902
1822 1861 5 0.005640349 42.36466
1823 1862 2 0.002256140 42.36692
1824 1863 8 0.009024558 42.37594
1825 1864 6 0.006768419 42.38271
1826 1865 3 0.003384209 42.38609
1827 1866 1 0.001128070 42.38722
1828 1867 2 0.002256140 42.38948
1829 1868 3 0.003384209 42.39286
1830 1869 2 0.002256140 42.39512
1831 1870 9 0.010152628 42.40527
1832 1871 8 0.009024558 42.41429
1833 1872 23 0.025945604 42.44024
1834 1873 10 0.011280698 42.45152
1835 1874 7 0.007896488 42.45942
1836 1875 10 0.011280698 42.47070
1837 1876 4 0.004512279 42.47521
1838 1877 3 0.003384209 42.47859
1839 1878 11 0.012408767 42.49100
1840 1879 17 0.019177186 42.51018
1841 1880 6 0.006768419 42.51695
1842 1881 3 0.003384209 42.52033
1843 1882 4 0.004512279 42.52485
1844 1883 6 0.006768419 42.53161
1845 1884 8 0.009024558 42.54064
1846 1885 9 0.010152628 42.55079
1847 1886 9 0.010152628 42.56094
1848 1887 3 0.003384209 42.56433
1849 1888 2 0.002256140 42.56658
1850 1889 4 0.004512279 42.57110
1851 1890 2 0.002256140 42.57335
1852 1891 6 0.006768419 42.58012
1853 1892 7 0.007896488 42.58802
1854 1893 18 0.020305256 42.60832
1855 1894 13 0.014664907 42.62299
1856 1895 9 0.010152628 42.63314
1857 1896 6 0.006768419 42.63991
1858 1897 6 0.006768419 42.64668
1859 1898 5 0.005640349 42.65232
1860 1899 8 0.009024558 42.66134
1861 1900 16 0.018049116 42.67939
1862 1901 1 0.001128070 42.68052
1863 1902 11 0.012408767 42.69293
1864 1903 1 0.001128070 42.69406
1865 1904 6 0.006768419 42.70082
1866 1905 2 0.002256140 42.70308
1867 1906 2 0.002256140 42.70534
1868 1907 4 0.004512279 42.70985
1869 1908 4 0.004512279 42.71436
1870 1909 3 0.003384209 42.71775
1871 1910 2 0.002256140 42.72000
1872 1911 7 0.007896488 42.72790
1873 1912 1 0.001128070 42.72903
1874 1913 3 0.003384209 42.73241
1875 1914 11 0.012408767 42.74482
1876 1915 7 0.007896488 42.75272
1877 1916 3 0.003384209 42.75610
1878 1917 1 0.001128070 42.75723
1879 1918 4 0.004512279 42.76174
1880 1919 3 0.003384209 42.76512
1881 1920 3 0.003384209 42.76851
1882 1921 5 0.005640349 42.77415
1883 1922 5 0.005640349 42.77979
1884 1923 2 0.002256140 42.78205
1885 1924 3 0.003384209 42.78543
1886 1925 2 0.002256140 42.78769
1887 1926 5 0.005640349 42.79333
1888 1927 14 0.015792977 42.80912
1889 1928 1 0.001128070 42.81025
1890 1929 2 0.002256140 42.81250
1891 1930 4 0.004512279 42.81702
1892 1931 2 0.002256140 42.81927
1893 1932 4 0.004512279 42.82378
1894 1933 2 0.002256140 42.82604
1895 1934 1 0.001128070 42.82717
1896 1935 8 0.009024558 42.83619
1897 1936 16 0.018049116 42.85424
1898 1937 1 0.001128070 42.85537
1899 1938 2 0.002256140 42.85763
1900 1939 1 0.001128070 42.85875
1901 1940 10 0.011280698 42.87004
1902 1941 3 0.003384209 42.87342
1903 1942 2 0.002256140 42.87568
1904 1943 3 0.003384209 42.87906
1905 1944 6 0.006768419 42.88583
1906 1945 8 0.009024558 42.89485
1907 1946 3 0.003384209 42.89824
1908 1947 6 0.006768419 42.90501
1909 1948 9 0.010152628 42.91516
1910 1949 4 0.004512279 42.91967
1911 1950 2 0.002256140 42.92193
1912 1951 2 0.002256140 42.92418
1913 1952 4 0.004512279 42.92869
1914 1953 4 0.004512279 42.93321
1915 1954 5 0.005640349 42.93885
1916 1955 6 0.006768419 42.94562
1917 1956 12 0.013536837 42.95915
1918 1957 17 0.019177186 42.97833
1919 1958 7 0.007896488 42.98623
1920 1959 3 0.003384209 42.98961
1921 1960 5 0.005640349 42.99525
1922 1961 9 0.010152628 43.00540
1923 1962 19 0.021433325 43.02684
1924 1963 7 0.007896488 43.03473
1925 1964 5 0.005640349 43.04037
1926 1965 5 0.005640349 43.04601
1927 1966 3 0.003384209 43.04940
1928 1967 8 0.009024558 43.05842
1929 1968 10 0.011280698 43.06970
1930 1969 10 0.011280698 43.08098
1931 1970 11 0.012408767 43.09339
1932 1971 11 0.012408767 43.10580
1933 1972 6 0.006768419 43.11257
1934 1973 7 0.007896488 43.12047
1935 1974 1 0.001128070 43.12159
1936 1975 35 0.039482442 43.16108
1937 1976 22 0.024817535 43.18589
1938 1977 14 0.015792977 43.20169
1939 1978 26 0.029329814 43.23102
1940 1979 3 0.003384209 43.23440
1941 1980 2 0.002256140 43.23666
1942 1981 1 0.001128070 43.23779
1943 1982 10 0.011280698 43.24907
1944 1983 6 0.006768419 43.25583
1945 1984 4 0.004512279 43.26035
1946 1985 3 0.003384209 43.26373
1947 1986 2 0.002256140 43.26599
1948 1987 1 0.001128070 43.26712
1949 1988 8 0.009024558 43.27614
1950 1989 2 0.002256140 43.27840
1951 1990 1 0.001128070 43.27952
1952 1991 1 0.001128070 43.28065
1953 1992 5 0.005640349 43.28629
1954 1993 5 0.005640349 43.29193
1955 1994 4 0.004512279 43.29645
1956 1995 8 0.009024558 43.30547
1957 1996 6 0.006768419 43.31224
1958 1997 23 0.025945604 43.33818
1959 1998 8 0.009024558 43.34721
1960 1999 8 0.009024558 43.35623
1961 2000 1 0.001128070 43.35736
1962 2001 1 0.001128070 43.35849
1963 2002 3 0.003384209 43.36187
1964 2003 3 0.003384209 43.36526
1965 2004 3 0.003384209 43.36864
1966 2005 2 0.002256140 43.37090
1967 2006 5 0.005640349 43.37654
1968 2007 1 0.001128070 43.37767
1969 2008 6 0.006768419 43.38443
1970 2009 6 0.006768419 43.39120
1971 2010 1 0.001128070 43.39233
1972 2011 11 0.012408767 43.40474
1973 2012 16 0.018049116 43.42279
1974 2013 12 0.013536837 43.43633
1975 2014 6 0.006768419 43.44309
1976 2015 5 0.005640349 43.44873
1977 2016 6 0.006768419 43.45550
1978 2017 4 0.004512279 43.46002
1979 2018 5 0.005640349 43.46566
1980 2019 16 0.018049116 43.48371
1981 2020 3 0.003384209 43.48709
1982 2021 3 0.003384209 43.49047
1983 2022 4 0.004512279 43.49499
1984 2023 8 0.009024558 43.50401
1985 2024 15 0.016921046 43.52093
1986 2025 3 0.003384209 43.52432
1987 2026 3 0.003384209 43.52770
1988 2027 5 0.005640349 43.53334
1989 2028 4 0.004512279 43.53785
1990 2029 1 0.001128070 43.53898
1991 2030 6 0.006768419 43.54575
1992 2031 9 0.010152628 43.55590
1993 2032 6 0.006768419 43.56267
1994 2033 32 0.036098232 43.59877
1995 2034 5 0.005640349 43.60441
1996 2035 2 0.002256140 43.60666
1997 2036 3 0.003384209 43.61005
1998 2037 2 0.002256140 43.61230
1999 2039 10 0.011280698 43.62359
2000 2040 3 0.003384209 43.62697
2001 2041 7 0.007896488 43.63487
2002 2042 5 0.005640349 43.64051
2003 2043 15 0.016921046 43.65743
2004 2044 14 0.015792977 43.67322
2005 2045 7 0.007896488 43.68112
2006 2046 6 0.006768419 43.68789
2007 2047 7 0.007896488 43.69578
2008 2048 4 0.004512279 43.70029
2009 2049 6 0.006768419 43.70706
2010 2050 2 0.002256140 43.70932
2011 2051 6 0.006768419 43.71609
2012 2052 4 0.004512279 43.72060
2013 2053 8 0.009024558 43.72962
2014 2054 5 0.005640349 43.73526
2015 2055 17 0.019177186 43.75444
2016 2056 4 0.004512279 43.75895
2017 2057 7 0.007896488 43.76685
2018 2058 7 0.007896488 43.77475
2019 2059 2 0.002256140 43.77700
2020 2060 12 0.013536837 43.79054
2021 2061 1 0.001128070 43.79167
2022 2062 5 0.005640349 43.79731
2023 2063 5 0.005640349 43.80295
2024 2064 7 0.007896488 43.81085
2025 2065 6 0.006768419 43.81761
2026 2066 8 0.009024558 43.82664
2027 2067 11 0.012408767 43.83905
2028 2068 1 0.001128070 43.84018
2029 2069 2 0.002256140 43.84243
2030 2070 8 0.009024558 43.85146
2031 2072 3 0.003384209 43.85484
2032 2073 4 0.004512279 43.85935
2033 2074 3 0.003384209 43.86274
2034 2075 7 0.007896488 43.87063
2035 2076 1 0.001128070 43.87176
2036 2077 2 0.002256140 43.87402
2037 2078 7 0.007896488 43.88191
2038 2079 3 0.003384209 43.88530
2039 2080 9 0.010152628 43.89545
2040 2081 2 0.002256140 43.89771
2041 2082 3 0.003384209 43.90109
2042 2083 3 0.003384209 43.90448
2043 2084 5 0.005640349 43.91012
2044 2085 13 0.014664907 43.92478
2045 2086 22 0.024817535 43.94960
2046 2087 4 0.004512279 43.95411
2047 2088 7 0.007896488 43.96201
2048 2089 9 0.010152628 43.97216
2049 2090 8 0.009024558 43.98118
2050 2091 6 0.006768419 43.98795
2051 2092 10 0.011280698 43.99923
2052 2093 5 0.005640349 44.00487
2053 2094 3 0.003384209 44.00826
2054 2095 9 0.010152628 44.01841
2055 2096 4 0.004512279 44.02292
2056 2097 1 0.001128070 44.02405
2057 2098 2 0.002256140 44.02631
2058 2099 2 0.002256140 44.02856
2059 2100 4 0.004512279 44.03308
2060 2101 3 0.003384209 44.03646
2061 2102 4 0.004512279 44.04097
2062 2103 10 0.011280698 44.05225
2063 2104 1 0.001128070 44.05338
2064 2105 2 0.002256140 44.05564
2065 2106 4 0.004512279 44.06015
2066 2108 1 0.001128070 44.06128
2067 2109 4 0.004512279 44.06579
2068 2110 2 0.002256140 44.06805
2069 2111 4 0.004512279 44.07256
2070 2112 9 0.010152628 44.08271
2071 2113 18 0.020305256 44.10302
2072 2114 5 0.005640349 44.10866
2073 2115 2 0.002256140 44.11091
2074 2116 3 0.003384209 44.11430
2075 2118 4 0.004512279 44.11881
2076 2119 4 0.004512279 44.12332
2077 2120 2 0.002256140 44.12558
2078 2121 2 0.002256140 44.12783
2079 2122 3 0.003384209 44.13122
2080 2123 7 0.007896488 44.13911
2081 2124 9 0.010152628 44.14927
2082 2125 2 0.002256140 44.15152
2083 2126 6 0.006768419 44.15829
2084 2127 12 0.013536837 44.17183
2085 2128 7 0.007896488 44.17972
2086 2129 5 0.005640349 44.18536
2087 2130 3 0.003384209 44.18875
2088 2131 8 0.009024558 44.19777
2089 2132 3 0.003384209 44.20116
2090 2133 3 0.003384209 44.20454
2091 2134 2 0.002256140 44.20680
2092 2135 3 0.003384209 44.21018
2093 2136 2 0.002256140 44.21244
2094 2137 9 0.010152628 44.22259
2095 2138 4 0.004512279 44.22710
2096 2139 1 0.001128070 44.22823
2097 2140 7 0.007896488 44.23613
2098 2141 5 0.005640349 44.24177
2099 2142 11 0.012408767 44.25418
2100 2143 4 0.004512279 44.25869
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8445 8598 3 0.003384209 99.04678
8446 8599 2 0.002256140 99.04904
8447 8600 1 0.001128070 99.05017
8448 8601 3 0.003384209 99.05355
8449 8605 1 0.001128070 99.05468
8450 8608 1 0.001128070 99.05581
8451 8609 1 0.001128070 99.05693
8452 8610 2 0.002256140 99.05919
8453 8611 1 0.001128070 99.06032
8454 8612 1 0.001128070 99.06145
8455 8613 1 0.001128070 99.06257
8456 8614 1 0.001128070 99.06370
8457 8615 1 0.001128070 99.06483
8458 8616 2 0.002256140 99.06709
8459 8617 5 0.005640349 99.07273
8460 8618 2 0.002256140 99.07498
8461 8619 3 0.003384209 99.07837
8462 8620 3 0.003384209 99.08175
8463 8621 4 0.004512279 99.08626
8464 8622 2 0.002256140 99.08852
8465 8623 1 0.001128070 99.08965
8466 8624 1 0.001128070 99.09078
8467 8625 2 0.002256140 99.09303
8468 8626 1 0.001128070 99.09416
8469 8627 2 0.002256140 99.09642
8470 8630 1 0.001128070 99.09754
8471 8631 1 0.001128070 99.09867
8472 8632 1 0.001128070 99.09980
8473 8635 1 0.001128070 99.10093
8474 8636 2 0.002256140 99.10318
8475 8639 1 0.001128070 99.10431
8476 8640 1 0.001128070 99.10544
8477 8642 2 0.002256140 99.10770
8478 8644 3 0.003384209 99.11108
8479 8645 3 0.003384209 99.11447
8480 8646 7 0.007896488 99.12236
8481 8647 6 0.006768419 99.12913
8482 8648 4 0.004512279 99.13364
8483 8649 2 0.002256140 99.13590
8484 8651 3 0.003384209 99.13928
8485 8652 2 0.002256140 99.14154
8486 8653 1 0.001128070 99.14267
8487 8654 1 0.001128070 99.14380
8488 8655 2 0.002256140 99.14605
8489 8656 2 0.002256140 99.14831
8490 8657 1 0.001128070 99.14944
8491 8658 2 0.002256140 99.15169
8492 8659 2 0.002256140 99.15395
8493 8660 5 0.005640349 99.15959
8494 8661 5 0.005640349 99.16523
8495 8662 2 0.002256140 99.16748
8496 8663 1 0.001128070 99.16861
8497 8664 2 0.002256140 99.17087
8498 8665 3 0.003384209 99.17425
8499 8666 4 0.004512279 99.17877
8500 8667 1 0.001128070 99.17989
8501 8669 2 0.002256140 99.18215
8502 8672 1 0.001128070 99.18328
8503 8673 1 0.001128070 99.18441
8504 8675 2 0.002256140 99.18666
8505 8676 3 0.003384209 99.19005
8506 8677 6 0.006768419 99.19681
8507 8678 3 0.003384209 99.20020
8508 8679 3 0.003384209 99.20358
8509 8680 4 0.004512279 99.20810
8510 8682 2 0.002256140 99.21035
8511 8684 1 0.001128070 99.21148
8512 8685 1 0.001128070 99.21261
8513 8688 1 0.001128070 99.21374
8514 8689 2 0.002256140 99.21599
8515 8692 3 0.003384209 99.21938
8516 8693 3 0.003384209 99.22276
8517 8697 1 0.001128070 99.22389
8518 8698 2 0.002256140 99.22614
8519 8699 2 0.002256140 99.22840
8520 8700 3 0.003384209 99.23178
8521 8701 6 0.006768419 99.23855
8522 8702 5 0.005640349 99.24419
8523 8703 2 0.002256140 99.24645
8524 8704 1 0.001128070 99.24758
8525 8706 1 0.001128070 99.24871
8526 8707 1 0.001128070 99.24983
8527 8708 3 0.003384209 99.25322
8528 8709 1 0.001128070 99.25435
8529 8710 5 0.005640349 99.25999
8530 8711 2 0.002256140 99.26224
8531 8714 2 0.002256140 99.26450
8532 8716 1 0.001128070 99.26563
8533 8717 2 0.002256140 99.26788
8534 8718 1 0.001128070 99.26901
8535 8721 1 0.001128070 99.27014
8536 8722 30 0.033842093 99.30398
8537 8723 36 0.040610511 99.34459
8538 8724 42 0.047378930 99.39197
8539 8726 3 0.003384209 99.39535
8540 8728 5 0.005640349 99.40099
8541 8729 1 0.001128070 99.40212
8542 8730 3 0.003384209 99.40551
8543 8733 1 0.001128070 99.40664
8544 8736 6 0.006768419 99.41340
8545 8737 3 0.003384209 99.41679
8546 8738 4 0.004512279 99.42130
8547 8739 2 0.002256140 99.42356
8548 8740 2 0.002256140 99.42581
8549 8743 1 0.001128070 99.42694
8550 8746 1 0.001128070 99.42807
8551 8750 3 0.003384209 99.43145
8552 8751 2 0.002256140 99.43371
8553 8752 1 0.001128070 99.43484
8554 8753 1 0.001128070 99.43597
8555 8760 1 0.001128070 99.43709
8556 8761 1 0.001128070 99.43822
8557 8762 1 0.001128070 99.43935
8558 8763 3 0.003384209 99.44273
8559 8764 2 0.002256140 99.44499
8560 8765 2 0.002256140 99.44725
8561 8766 1 0.001128070 99.44837
8562 8769 1 0.001128070 99.44950
8563 8770 1 0.001128070 99.45063
8564 8771 1 0.001128070 99.45176
8565 8772 1 0.001128070 99.45289
8566 8773 1 0.001128070 99.45401
8567 8777 2 0.002256140 99.45627
8568 8778 2 0.002256140 99.45853
8569 8779 1 0.001128070 99.45965
8570 8780 3 0.003384209 99.46304
8571 8784 1 0.001128070 99.46417
8572 8790 1 0.001128070 99.46529
8573 8791 4 0.004512279 99.46981
8574 8805 1 0.001128070 99.47094
8575 8807 1 0.001128070 99.47206
8576 8811 1 0.001128070 99.47319
8577 8817 1 0.001128070 99.47432
8578 8818 1 0.001128070 99.47545
8579 8830 1 0.001128070 99.47658
8580 8832 1 0.001128070 99.47770
8581 8838 2 0.002256140 99.47996
8582 8840 1 0.001128070 99.48109
8583 8841 1 0.001128070 99.48222
8584 8843 1 0.001128070 99.48334
8585 8847 2 0.002256140 99.48560
8586 8848 2 0.002256140 99.48786
8587 8849 4 0.004512279 99.49237
8588 8850 2 0.002256140 99.49462
8589 8853 2 0.002256140 99.49688
8590 8855 6 0.006768419 99.50365
8591 8856 9 0.010152628 99.51380
8592 8857 9 0.010152628 99.52395
8593 8867 1 0.001128070 99.52508
8594 8868 1 0.001128070 99.52621
8595 8869 1 0.001128070 99.52734
8596 8870 1 0.001128070 99.52847
8597 8875 1 0.001128070 99.52959
8598 8880 1 0.001128070 99.53072
8599 8882 1 0.001128070 99.53185
8600 8883 1 0.001128070 99.53298
8601 8885 1 0.001128070 99.53411
8602 8886 1 0.001128070 99.53524
8603 8888 2 0.002256140 99.53749
8604 8889 1 0.001128070 99.53862
8605 8890 2 0.002256140 99.54088
8606 8891 1 0.001128070 99.54200
8607 8897 3 0.003384209 99.54539
8608 8898 1 0.001128070 99.54652
8609 8902 1 0.001128070 99.54764
8610 8914 1 0.001128070 99.54877
8611 8916 1 0.001128070 99.54990
8612 8921 1 0.001128070 99.55103
8613 8924 1 0.001128070 99.55216
8614 8930 2 0.002256140 99.55441
8615 8931 1 0.001128070 99.55554
8616 8933 1 0.001128070 99.55667
8617 8935 2 0.002256140 99.55892
8618 8936 1 0.001128070 99.56005
8619 8937 1 0.001128070 99.56118
8620 8939 1 0.001128070 99.56231
8621 8940 2 0.002256140 99.56457
8622 8941 6 0.006768419 99.57133
8623 8942 4 0.004512279 99.57585
8624 8943 1 0.001128070 99.57697
8625 8945 1 0.001128070 99.57810
8626 8953 1 0.001128070 99.57923
8627 8960 1 0.001128070 99.58036
8628 8976 1 0.001128070 99.58149
8629 8978 1 0.001128070 99.58261
8630 8981 1 0.001128070 99.58374
8631 8982 2 0.002256140 99.58600
8632 8983 2 0.002256140 99.58825
8633 8984 4 0.004512279 99.59277
8634 8997 1 0.001128070 99.59389
8635 8998 3 0.003384209 99.59728
8636 8999 2 0.002256140 99.59954
8637 9000 1 0.001128070 99.60066
8638 9010 1 0.001128070 99.60179
8639 9015 1 0.001128070 99.60292
8640 9021 1 0.001128070 99.60405
8641 9025 5 0.005640349 99.60969
8642 9026 3 0.003384209 99.61307
8643 9034 1 0.001128070 99.61420
8644 9035 1 0.001128070 99.61533
8645 9044 1 0.001128070 99.61646
8646 9046 2 0.002256140 99.61871
8647 9049 1 0.001128070 99.61984
8648 9051 1 0.001128070 99.62097
8649 9056 1 0.001128070 99.62210
8650 9059 2 0.002256140 99.62435
8651 9061 1 0.001128070 99.62548
8652 9062 4 0.004512279 99.62999
8653 9066 1 0.001128070 99.63112
8654 9067 1 0.001128070 99.63225
8655 9085 3 0.003384209 99.63563
8656 9086 2 0.002256140 99.63789
8657 9087 2 0.002256140 99.64015
8658 9089 2 0.002256140 99.64240
8659 9090 1 0.001128070 99.64353
8660 9103 1 0.001128070 99.64466
8661 9104 1 0.001128070 99.64579
8662 9107 2 0.002256140 99.64804
8663 9108 2 0.002256140 99.65030
8664 9114 1 0.001128070 99.65143
8665 9118 1 0.001128070 99.65255
8666 9121 1 0.001128070 99.65368
8667 9122 2 0.002256140 99.65594
8668 9129 1 0.001128070 99.65707
8669 9135 1 0.001128070 99.65819
8670 9136 1 0.001128070 99.65932
8671 9141 1 0.001128070 99.66045
8672 9142 1 0.001128070 99.66158
8673 9143 5 0.005640349 99.66722
8674 9146 1 0.001128070 99.66835
8675 9176 1 0.001128070 99.66948
8676 9189 1 0.001128070 99.67060
8677 9212 3 0.003384209 99.67399
8678 9213 2 0.002256140 99.67624
8679 9219 2 0.002256140 99.67850
8680 9220 1 0.001128070 99.67963
8681 9221 3 0.003384209 99.68301
8682 9234 1 0.001128070 99.68414
8683 9235 1 0.001128070 99.68527
8684 9236 1 0.001128070 99.68640
8685 9237 1 0.001128070 99.68752
8686 9241 2 0.002256140 99.68978
8687 9253 1 0.001128070 99.69091
8688 9255 6 0.006768419 99.69768
8689 9263 3 0.003384209 99.70106
8690 9272 1 0.001128070 99.70219
8691 9275 1 0.001128070 99.70332
8692 9296 1 0.001128070 99.70445
8693 9298 2 0.002256140 99.70670
8694 9304 2 0.002256140 99.70896
8695 9317 1 0.001128070 99.71009
8696 9338 2 0.002256140 99.71234
8697 9339 2 0.002256140 99.71460
8698 9340 6 0.006768419 99.72137
8699 9359 1 0.001128070 99.72249
8700 9375 2 0.002256140 99.72475
8701 9376 2 0.002256140 99.72701
8702 9379 1 0.001128070 99.72814
8703 9380 1 0.001128070 99.72926
8704 9381 1 0.001128070 99.73039
8705 9383 2 0.002256140 99.73265
8706 9397 1 0.001128070 99.73378
8707 9398 1 0.001128070 99.73490
8708 9414 1 0.001128070 99.73603
8709 9419 1 0.001128070 99.73716
8710 9447 1 0.001128070 99.73829
8711 9448 25 0.028201744 99.76649
8712 9449 14 0.015792977 99.78228
8713 9450 11 0.012408767 99.79469
8714 9463 2 0.002256140 99.79695
8715 9470 1 0.001128070 99.79808
8716 9509 1 0.001128070 99.79920
8717 9526 1 0.001128070 99.80033
8718 9541 1 0.001128070 99.80146
8719 9555 1 0.001128070 99.80259
8720 9556 1 0.001128070 99.80372
8721 9561 1 0.001128070 99.80484
8722 9606 5 0.005640349 99.81048
8723 9607 2 0.002256140 99.81274
8724 9608 2 0.002256140 99.81500
8725 9621 1 0.001128070 99.81612
8726 9638 1 0.001128070 99.81725
8727 9656 1 0.001128070 99.81838
8728 9666 1 0.001128070 99.81951
8729 9763 1 0.001128070 99.82064
8730 9764 3 0.003384209 99.82402
8731 9765 4 0.004512279 99.82853
8732 9766 3 0.003384209 99.83192
8733 9795 1 0.001128070 99.83305
8734 9817 1 0.001128070 99.83417
8735 9819 1 0.001128070 99.83530
8736 9820 1 0.001128070 99.83643
8737 9873 1 0.001128070 99.83756
8738 9874 2 0.002256140 99.83981
8739 9879 2 0.002256140 99.84207
8740 9900 1 0.001128070 99.84320
8741 9912 1 0.001128070 99.84433
8742 9913 1 0.001128070 99.84545
8743 9919 1 0.001128070 99.84658
8744 9920 4 0.004512279 99.85109
8745 9921 3 0.003384209 99.85448
8746 9975 1 0.001128070 99.85561
8747 9976 1 0.001128070 99.85674
8748 10016 1 0.001128070 99.85786
8749 10017 1 0.001128070 99.85899
8750 10133 1 0.001128070 99.86012
8751 10136 1 0.001128070 99.86125
8752 10138 1 0.001128070 99.86238
8753 10158 1 0.001128070 99.86350
8754 10170 1 0.001128070 99.86463
8755 10220 1 0.001128070 99.86576
8756 10221 1 0.001128070 99.86689
8757 10253 4 0.004512279 99.87140
8758 10290 1 0.001128070 99.87253
8759 10388 1 0.001128070 99.87366
8760 10434 1 0.001128070 99.87478
8761 10532 1 0.001128070 99.87591
8762 10544 2 0.002256140 99.87817
8763 10554 1 0.001128070 99.87930
8764 10567 1 0.001128070 99.88042
8765 10654 1 0.001128070 99.88155
8766 10672 22 0.024817535 99.90637
8767 10673 24 0.027073674 99.93344
8768 10674 34 0.038354372 99.97180
8769 10695 1 0.001128070 99.97293
8770 10837 2 0.002256140 99.97518
8771 10898 1 0.001128070 99.97631
8772 10908 1 0.001128070 99.97744
8773 11142 1 0.001128070 99.97857
8774 11145 1 0.001128070 99.97969
8775 11242 1 0.001128070 99.98082
8776 11243 2 0.002256140 99.98308
8777 11425 1 0.001128070 99.98421
8778 11465 1 0.001128070 99.98534
8779 11549 1 0.001128070 99.98646
8780 11550 2 0.002256140 99.98872
8781 11621 1 0.001128070 99.98985
8782 11643 1 0.001128070 99.99098
8783 11644 1 0.001128070 99.99210
8784 11875 1 0.001128070 99.99323
8785 11887 1 0.001128070 99.99436
8786 11902 1 0.001128070 99.99549
8787 12146 1 0.001128070 99.99662
8788 12153 1 0.001128070 99.99774
8789 12210 1 0.001128070 99.99887
8790 17775 1 0.001128070 100.00000
phishing_bruto |> group_by(time_domain_activation) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 12,841 × 4
# Groups: time_domain_activation [8,790]
time_domain_activation phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 13492 55.4
2 -1 1 10862 44.6
3 1 0 1 100
4 2 0 1 16.7
5 2 1 5 83.3
6 3 0 2 12.5
7 3 1 14 87.5
8 4 0 4 18.2
9 4 1 18 81.8
10 5 0 7 24.1
# … with 12,831 more rows
min max
1 -1 17775
Fijándonos primeramente en los valores que puede tomar la variable, vemos que el valor mínimo que puede tomar esta variable es el -1. En este caso, dicho valor indica que la característica en cuestión (tiempo de activación del dominio), no se aplica a determinadas observaciones. Por otro lado, en cuanto al máximo, tenemos un número de días que a priori parece ser demasiado elevado. Convendría observar la distribución de la variable:
# Visualización
ggplot(phishing_bruto, aes(x = time_domain_activation)) +
geom_density(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por tiempo de la activación del dominio",
x = "Tiempo de la activación del dominio (en días)", y = "Densidad", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Parece que a partir de un valor de 10000 días aproximadamente, apenas podemos visualizar datos, y parece que los registros con valores superiores, presentan únicamente webs legítimas:
phishing_bruto |> filter(time_domain_activation >= 10000) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
phishing n porc
1 0 126 99.2125984
2 1 1 0.7874016
Por lo tanto, teniendo esto en cuenta junto a su distribución asimétrica, podríamos imputarle 1000 a todo lo que supere dicho valor, ya que tener números tan elevados en esta variable y con muy pocos registros, únicamente aumentaría la complejidad del modelo.
# Anañlisis cuantitativo
phishing_bruto |> count(time_domain_expiration) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
time_domain_expiration n porc cumul
1 -1 26210 29.566708405 29.56671
2 0 66 0.074452604 29.64116
3 1 111 0.125215743 29.76638
4 2 75 0.084605232 29.85098
5 3 68 0.076708744 29.92769
6 4 78 0.087989441 30.01568
7 5 109 0.122959604 30.13864
8 6 107 0.120703464 30.25934
9 7 118 0.133112232 30.39246
10 8 56 0.063171907 30.45563
11 9 81 0.091373651 30.54700
12 10 54 0.060915767 30.60792
13 11 89 0.100398209 30.70832
14 12 65 0.073324534 30.78164
15 13 41 0.046250860 30.82789
16 14 96 0.108294697 30.93619
17 15 83 0.093629790 31.02981
18 16 83 0.093629790 31.12344
19 17 77 0.086861372 31.21031
20 18 66 0.074452604 31.28476
21 19 97 0.109422767 31.39418
22 20 98 0.110550836 31.50473
23 21 81 0.091373651 31.59611
24 22 69 0.077836813 31.67394
25 23 69 0.077836813 31.75178
26 24 83 0.093629790 31.84541
27 25 96 0.108294697 31.95370
28 26 82 0.092501720 32.04621
29 27 116 0.130856092 32.17706
30 28 114 0.128599953 32.30566
31 29 82 0.092501720 32.39816
32 30 76 0.085733302 32.48390
33 31 87 0.098142069 32.58204
34 32 107 0.120703464 32.70274
35 33 124 0.139880650 32.84262
36 34 112 0.126343813 32.96897
37 35 87 0.098142069 33.06711
38 36 91 0.102654348 33.16976
39 37 87 0.098142069 33.26791
40 38 101 0.113935046 33.38184
41 39 106 0.119575395 33.50142
42 40 105 0.118447325 33.61986
43 41 69 0.077836813 33.69770
44 42 95 0.107166627 33.80487
45 43 132 0.148905208 33.95377
46 44 116 0.130856092 34.08463
47 45 113 0.127471883 34.21210
48 46 129 0.145520999 34.35762
49 47 101 0.113935046 34.47156
50 48 122 0.137624511 34.60918
51 49 78 0.087989441 34.69717
52 50 107 0.120703464 34.81787
53 51 73 0.082349092 34.90022
54 52 83 0.093629790 34.99385
55 53 71 0.080092953 35.07394
56 54 110 0.124087674 35.19803
57 55 117 0.131984162 35.33002
58 56 105 0.118447325 35.44846
59 57 131 0.147777139 35.59624
60 58 123 0.138752580 35.73499
61 59 103 0.116191185 35.85119
62 60 137 0.154545557 36.00573
63 61 82 0.092501720 36.09823
64 62 158 0.178235022 36.27647
65 63 110 0.124087674 36.40056
66 64 70 0.078964883 36.47952
67 65 97 0.109422767 36.58894
68 66 107 0.120703464 36.70965
69 67 100 0.112806976 36.82245
70 68 120 0.135368371 36.95782
71 69 94 0.106038557 37.06386
72 70 115 0.129728022 37.19359
73 71 138 0.155673627 37.34926
74 72 110 0.124087674 37.47335
75 73 157 0.177106952 37.65046
76 74 124 0.139880650 37.79034
77 75 118 0.133112232 37.92345
78 76 95 0.107166627 38.03062
79 77 110 0.124087674 38.15470
80 78 94 0.106038557 38.26074
81 79 117 0.131984162 38.39273
82 80 99 0.111678906 38.50441
83 81 90 0.101526278 38.60593
84 82 69 0.077836813 38.68377
85 83 67 0.075580674 38.75935
86 84 112 0.126343813 38.88569
87 85 103 0.116191185 39.00188
88 86 111 0.125215743 39.12710
89 87 102 0.115063116 39.24216
90 88 119 0.134240301 39.37640
91 89 115 0.129728022 39.50613
92 90 99 0.111678906 39.61781
93 91 116 0.130856092 39.74867
94 92 129 0.145520999 39.89419
95 93 123 0.138752580 40.03294
96 94 117 0.131984162 40.16492
97 95 92 0.103782418 40.26871
98 96 81 0.091373651 40.36008
99 97 109 0.122959604 40.48304
100 98 118 0.133112232 40.61615
101 99 122 0.137624511 40.75378
102 100 103 0.116191185 40.86997
103 101 99 0.111678906 40.98165
104 102 98 0.110550836 41.09220
105 103 92 0.103782418 41.19598
106 104 137 0.154545557 41.35053
107 105 103 0.116191185 41.46672
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1643 1644 6 0.006768419 95.36815
1644 1645 3 0.003384209 95.37153
1645 1646 7 0.007896488 95.37943
1646 1647 8 0.009024558 95.38845
1647 1648 7 0.007896488 95.39635
1648 1649 11 0.012408767 95.40876
1649 1650 7 0.007896488 95.41665
1650 1651 10 0.011280698 95.42793
1651 1652 6 0.006768419 95.43470
1652 1653 9 0.010152628 95.44485
1653 1654 8 0.009024558 95.45388
1654 1655 6 0.006768419 95.46065
1655 1656 3 0.003384209 95.46403
1656 1657 6 0.006768419 95.47080
1657 1658 3 0.003384209 95.47418
1658 1659 4 0.004512279 95.47870
1659 1660 6 0.006768419 95.48546
1660 1661 3 0.003384209 95.48885
1661 1662 5 0.005640349 95.49449
1662 1663 22 0.024817535 95.51931
1663 1664 3 0.003384209 95.52269
1664 1665 2 0.002256140 95.52495
1665 1666 5 0.005640349 95.53059
1666 1667 8 0.009024558 95.53961
1667 1668 7 0.007896488 95.54751
1668 1669 4 0.004512279 95.55202
1669 1670 2 0.002256140 95.55428
1670 1671 8 0.009024558 95.56330
1671 1672 11 0.012408767 95.57571
1672 1673 3 0.003384209 95.57909
1673 1674 11 0.012408767 95.59150
1674 1675 3 0.003384209 95.59489
1675 1676 7 0.007896488 95.60278
1676 1677 8 0.009024558 95.61181
1677 1678 3 0.003384209 95.61519
1678 1679 3 0.003384209 95.61858
1679 1680 6 0.006768419 95.62535
1680 1681 3 0.003384209 95.62873
1681 1682 6 0.006768419 95.63550
1682 1683 8 0.009024558 95.64452
1683 1684 5 0.005640349 95.65016
1684 1685 3 0.003384209 95.65355
1685 1686 2 0.002256140 95.65580
1686 1688 7 0.007896488 95.66370
1687 1689 3 0.003384209 95.66708
1688 1690 4 0.004512279 95.67160
1689 1691 7 0.007896488 95.67949
1690 1692 5 0.005640349 95.68513
1691 1693 2 0.002256140 95.68739
1692 1694 2 0.002256140 95.68965
1693 1695 8 0.009024558 95.69867
1694 1696 6 0.006768419 95.70544
1695 1697 5 0.005640349 95.71108
1696 1698 12 0.013536837 95.72462
1697 1699 4 0.004512279 95.72913
1698 1700 7 0.007896488 95.73702
1699 1701 3 0.003384209 95.74041
1700 1702 7 0.007896488 95.74831
1701 1703 5 0.005640349 95.75395
1702 1704 8 0.009024558 95.76297
1703 1705 9 0.010152628 95.77312
1704 1706 13 0.014664907 95.78779
1705 1707 10 0.011280698 95.79907
1706 1708 7 0.007896488 95.80696
1707 1709 3 0.003384209 95.81035
1708 1710 4 0.004512279 95.81486
1709 1711 5 0.005640349 95.82050
1710 1712 6 0.006768419 95.82727
1711 1714 2 0.002256140 95.82953
1712 1715 4 0.004512279 95.83404
1713 1716 26 0.029329814 95.86337
1714 1718 6 0.006768419 95.87014
1715 1719 3 0.003384209 95.87352
1716 1720 4 0.004512279 95.87803
1717 1721 7 0.007896488 95.88593
1718 1722 4 0.004512279 95.89044
1719 1723 2 0.002256140 95.89270
1720 1724 2 0.002256140 95.89495
1721 1725 4 0.004512279 95.89947
1722 1726 2 0.002256140 95.90172
1723 1727 5 0.005640349 95.90736
1724 1728 6 0.006768419 95.91413
1725 1729 8 0.009024558 95.92316
1726 1730 7 0.007896488 95.93105
1727 1731 7 0.007896488 95.93895
1728 1732 5 0.005640349 95.94459
1729 1733 1 0.001128070 95.94572
1730 1734 3 0.003384209 95.94910
1731 1735 6 0.006768419 95.95587
1732 1736 6 0.006768419 95.96264
1733 1737 3 0.003384209 95.96602
1734 1738 1 0.001128070 95.96715
1735 1739 2 0.002256140 95.96941
1736 1740 3 0.003384209 95.97279
1737 1741 5 0.005640349 95.97843
1738 1742 1 0.001128070 95.97956
1739 1744 4 0.004512279 95.98407
1740 1745 3 0.003384209 95.98746
1741 1746 7 0.007896488 95.99535
1742 1747 6 0.006768419 96.00212
1743 1748 6 0.006768419 96.00889
1744 1749 4 0.004512279 96.01340
1745 1750 9 0.010152628 96.02355
1746 1751 10 0.011280698 96.03483
1747 1752 8 0.009024558 96.04386
1748 1753 4 0.004512279 96.04837
1749 1754 1 0.001128070 96.04950
1750 1756 4 0.004512279 96.05401
1751 1757 4 0.004512279 96.05852
1752 1758 4 0.004512279 96.06304
1753 1759 2 0.002256140 96.06529
1754 1760 3 0.003384209 96.06868
1755 1761 6 0.006768419 96.07545
1756 1762 1 0.001128070 96.07657
1757 1763 4 0.004512279 96.08109
1758 1764 4 0.004512279 96.08560
1759 1765 5 0.005640349 96.09124
1760 1766 14 0.015792977 96.10703
1761 1767 9 0.010152628 96.11718
1762 1768 5 0.005640349 96.12282
1763 1769 3 0.003384209 96.12621
1764 1770 5 0.005640349 96.13185
1765 1771 4 0.004512279 96.13636
1766 1772 2 0.002256140 96.13862
1767 1773 4 0.004512279 96.14313
1768 1774 4 0.004512279 96.14764
1769 1775 5 0.005640349 96.15328
1770 1776 1 0.001128070 96.15441
1771 1777 1 0.001128070 96.15554
1772 1778 18 0.020305256 96.17584
1773 1779 11 0.012408767 96.18825
1774 1780 7 0.007896488 96.19615
1775 1782 6 0.006768419 96.20292
1776 1783 1 0.001128070 96.20405
1777 1784 4 0.004512279 96.20856
1778 1785 6 0.006768419 96.21533
1779 1786 5 0.005640349 96.22097
1780 1787 3 0.003384209 96.22435
1781 1788 1 0.001128070 96.22548
1782 1789 1 0.001128070 96.22661
1783 1790 3 0.003384209 96.22999
1784 1791 4 0.004512279 96.23450
1785 1792 8 0.009024558 96.24353
1786 1793 6 0.006768419 96.25030
1787 1794 7 0.007896488 96.25819
1788 1795 1 0.001128070 96.25932
1789 1796 3 0.003384209 96.26270
1790 1797 1 0.001128070 96.26383
1791 1798 2 0.002256140 96.26609
1792 1799 5 0.005640349 96.27173
1793 1800 6 0.006768419 96.27850
1794 1801 3 0.003384209 96.28188
1795 1802 6 0.006768419 96.28865
1796 1803 1 0.001128070 96.28978
1797 1804 6 0.006768419 96.29655
1798 1805 1 0.001128070 96.29768
1799 1806 10 0.011280698 96.30896
1800 1807 5 0.005640349 96.31460
1801 1808 2 0.002256140 96.31685
1802 1809 5 0.005640349 96.32249
1803 1810 2 0.002256140 96.32475
1804 1811 6 0.006768419 96.33152
1805 1812 13 0.014664907 96.34618
1806 1813 2 0.002256140 96.34844
1807 1814 1 0.001128070 96.34957
1808 1815 5 0.005640349 96.35521
1809 1816 4 0.004512279 96.35972
1810 1817 4 0.004512279 96.36423
1811 1818 5 0.005640349 96.36987
1812 1819 3 0.003384209 96.37326
1813 1820 2 0.002256140 96.37551
1814 1821 2 0.002256140 96.37777
1815 1822 5 0.005640349 96.38341
1816 1823 7 0.007896488 96.39130
1817 1824 3 0.003384209 96.39469
1818 1825 8 0.009024558 96.40371
1819 1826 8 0.009024558 96.41274
1820 1827 23 0.025945604 96.43868
1821 1828 5 0.005640349 96.44432
1822 1829 5 0.005640349 96.44996
1823 1830 4 0.004512279 96.45448
1824 1831 5 0.005640349 96.46012
1825 1832 4 0.004512279 96.46463
1826 1833 4 0.004512279 96.46914
1827 1834 4 0.004512279 96.47365
1828 1835 9 0.010152628 96.48381
1829 1836 9 0.010152628 96.49396
1830 1837 7 0.007896488 96.50186
1831 1838 1 0.001128070 96.50298
1832 1839 3 0.003384209 96.50637
1833 1841 4 0.004512279 96.51088
1834 1842 8 0.009024558 96.51990
1835 1843 2 0.002256140 96.52216
1836 1844 1 0.001128070 96.52329
1837 1845 5 0.005640349 96.52893
1838 1846 2 0.002256140 96.53119
1839 1847 3 0.003384209 96.53457
1840 1848 2 0.002256140 96.53683
1841 1849 2 0.002256140 96.53908
1842 1850 1 0.001128070 96.54021
1843 1851 2 0.002256140 96.54247
1844 1852 5 0.005640349 96.54811
1845 1853 3 0.003384209 96.55149
1846 1854 2 0.002256140 96.55375
1847 1856 5 0.005640349 96.55939
1848 1857 5 0.005640349 96.56503
1849 1858 1 0.001128070 96.56616
1850 1859 1 0.001128070 96.56728
1851 1860 2 0.002256140 96.56954
1852 1861 1 0.001128070 96.57067
1853 1862 5 0.005640349 96.57631
1854 1863 3 0.003384209 96.57969
1855 1864 6 0.006768419 96.58646
1856 1865 2 0.002256140 96.58872
1857 1866 1 0.001128070 96.58985
1858 1867 1 0.001128070 96.59097
1859 1868 1 0.001128070 96.59210
1860 1869 1 0.001128070 96.59323
1861 1870 3 0.003384209 96.59661
1862 1871 3 0.003384209 96.60000
1863 1872 2 0.002256140 96.60225
1864 1873 3 0.003384209 96.60564
1865 1875 1 0.001128070 96.60677
1866 1876 1 0.001128070 96.60789
1867 1877 4 0.004512279 96.61241
1868 1878 4 0.004512279 96.61692
1869 1879 5 0.005640349 96.62256
1870 1880 3 0.003384209 96.62594
1871 1881 3 0.003384209 96.62933
1872 1882 2 0.002256140 96.63158
1873 1883 1 0.001128070 96.63271
1874 1884 2 0.002256140 96.63497
1875 1885 6 0.006768419 96.64174
1876 1886 3 0.003384209 96.64512
1877 1887 3 0.003384209 96.64850
1878 1889 1 0.001128070 96.64963
1879 1890 1 0.001128070 96.65076
1880 1891 4 0.004512279 96.65527
1881 1892 1 0.001128070 96.65640
1882 1894 2 0.002256140 96.65866
1883 1896 1 0.001128070 96.65979
1884 1897 2 0.002256140 96.66204
1885 1898 1 0.001128070 96.66317
1886 1899 2 0.002256140 96.66543
1887 1900 3 0.003384209 96.66881
1888 1901 3 0.003384209 96.67219
1889 1902 2 0.002256140 96.67445
1890 1903 1 0.001128070 96.67558
1891 1904 2 0.002256140 96.67783
1892 1907 3 0.003384209 96.68122
1893 1908 2 0.002256140 96.68347
1894 1909 1 0.001128070 96.68460
1895 1910 3 0.003384209 96.68799
1896 1911 3 0.003384209 96.69137
1897 1912 2 0.002256140 96.69363
1898 1913 1 0.001128070 96.69476
1899 1914 3 0.003384209 96.69814
1900 1915 1 0.001128070 96.69927
1901 1916 2 0.002256140 96.70152
1902 1917 1 0.001128070 96.70265
1903 1919 3 0.003384209 96.70604
1904 1920 2 0.002256140 96.70829
1905 1921 5 0.005640349 96.71393
1906 1922 5 0.005640349 96.71957
1907 1923 2 0.002256140 96.72183
1908 1924 5 0.005640349 96.72747
1909 1925 3 0.003384209 96.73085
1910 1926 1 0.001128070 96.73198
1911 1927 3 0.003384209 96.73537
1912 1928 1 0.001128070 96.73649
1913 1929 3 0.003384209 96.73988
1914 1930 2 0.002256140 96.74213
1915 1931 10 0.011280698 96.75342
1916 1932 11 0.012408767 96.76582
1917 1933 1 0.001128070 96.76695
1918 1934 4 0.004512279 96.77146
1919 1935 1 0.001128070 96.77259
1920 1936 3 0.003384209 96.77598
1921 1938 3 0.003384209 96.77936
1922 1939 2 0.002256140 96.78162
1923 1940 1 0.001128070 96.78275
1924 1942 1 0.001128070 96.78387
1925 1944 6 0.006768419 96.79064
1926 1945 4 0.004512279 96.79515
1927 1946 8 0.009024558 96.80418
1928 1947 3 0.003384209 96.80756
1929 1948 3 0.003384209 96.81095
1930 1949 2 0.002256140 96.81320
1931 1950 1 0.001128070 96.81433
1932 1951 1 0.001128070 96.81546
1933 1952 2 0.002256140 96.81772
1934 1953 4 0.004512279 96.82223
1935 1954 2 0.002256140 96.82448
1936 1955 2 0.002256140 96.82674
1937 1956 6 0.006768419 96.83351
1938 1957 1 0.001128070 96.83464
1939 1958 1 0.001128070 96.83576
1940 1959 2 0.002256140 96.83802
1941 1961 7 0.007896488 96.84592
1942 1962 1 0.001128070 96.84705
1943 1963 1 0.001128070 96.84817
1944 1964 1 0.001128070 96.84930
1945 1965 1 0.001128070 96.85043
1946 1966 2 0.002256140 96.85269
1947 1967 1 0.001128070 96.85381
1948 1968 1 0.001128070 96.85494
1949 1969 1 0.001128070 96.85607
1950 1970 1 0.001128070 96.85720
1951 1972 2 0.002256140 96.85945
1952 1977 1 0.001128070 96.86058
1953 1978 4 0.004512279 96.86509
1954 1980 4 0.004512279 96.86961
1955 1981 2 0.002256140 96.87186
1956 1982 3 0.003384209 96.87525
1957 1983 2 0.002256140 96.87750
1958 1984 5 0.005640349 96.88314
1959 1985 3 0.003384209 96.88653
1960 1986 3 0.003384209 96.88991
1961 1987 1 0.001128070 96.89104
1962 1988 1 0.001128070 96.89217
1963 1989 2 0.002256140 96.89442
1964 1991 2 0.002256140 96.89668
1965 1992 2 0.002256140 96.89894
1966 1995 2 0.002256140 96.90119
1967 1997 1 0.001128070 96.90232
1968 1998 2 0.002256140 96.90458
1969 1999 3 0.003384209 96.90796
1970 2000 1 0.001128070 96.90909
1971 2001 2 0.002256140 96.91134
1972 2002 2 0.002256140 96.91360
1973 2003 2 0.002256140 96.91586
1974 2004 2 0.002256140 96.91811
1975 2006 5 0.005640349 96.92375
1976 2008 4 0.004512279 96.92827
1977 2009 2 0.002256140 96.93052
1978 2010 2 0.002256140 96.93278
1979 2011 1 0.001128070 96.93391
1980 2013 3 0.003384209 96.93729
1981 2016 3 0.003384209 96.94067
1982 2017 1 0.001128070 96.94180
1983 2018 6 0.006768419 96.94857
1984 2019 3 0.003384209 96.95196
1985 2020 2 0.002256140 96.95421
1986 2022 2 0.002256140 96.95647
1987 2023 2 0.002256140 96.95872
1988 2024 1 0.001128070 96.95985
1989 2026 1 0.001128070 96.96098
1990 2027 2 0.002256140 96.96324
1991 2028 1 0.001128070 96.96436
1992 2031 1 0.001128070 96.96549
1993 2032 1 0.001128070 96.96662
1994 2034 1 0.001128070 96.96775
1995 2035 1 0.001128070 96.96888
1996 2036 2 0.002256140 96.97113
1997 2037 2 0.002256140 96.97339
1998 2039 2 0.002256140 96.97564
1999 2040 3 0.003384209 96.97903
2000 2043 4 0.004512279 96.98354
2001 2044 5 0.005640349 96.98918
2002 2045 3 0.003384209 96.99257
2003 2046 1 0.001128070 96.99369
2004 2050 2 0.002256140 96.99595
2005 2051 3 0.003384209 96.99933
2006 2052 1 0.001128070 97.00046
2007 2053 1 0.001128070 97.00159
2008 2054 1 0.001128070 97.00272
2009 2055 1 0.001128070 97.00385
2010 2056 1 0.001128070 97.00497
2011 2057 1 0.001128070 97.00610
2012 2058 2 0.002256140 97.00836
2013 2059 2 0.002256140 97.01062
2014 2060 3 0.003384209 97.01400
2015 2064 2 0.002256140 97.01626
2016 2065 3 0.003384209 97.01964
2017 2066 2 0.002256140 97.02190
2018 2067 2 0.002256140 97.02415
2019 2068 2 0.002256140 97.02641
2020 2069 2 0.002256140 97.02866
2021 2070 2 0.002256140 97.03092
2022 2071 1 0.001128070 97.03205
2023 2072 1 0.001128070 97.03318
2024 2073 2 0.002256140 97.03543
2025 2074 1 0.001128070 97.03656
2026 2075 2 0.002256140 97.03882
2027 2078 3 0.003384209 97.04220
2028 2079 2 0.002256140 97.04446
2029 2080 1 0.001128070 97.04559
2030 2081 1 0.001128070 97.04671
2031 2082 2 0.002256140 97.04897
2032 2083 2 0.002256140 97.05123
2033 2084 2 0.002256140 97.05348
2034 2085 2 0.002256140 97.05574
2035 2086 1 0.001128070 97.05687
2036 2087 2 0.002256140 97.05912
2037 2088 1 0.001128070 97.06025
2038 2089 2 0.002256140 97.06251
2039 2091 5 0.005640349 97.06815
2040 2093 2 0.002256140 97.07040
2041 2095 2 0.002256140 97.07266
2042 2098 1 0.001128070 97.07379
2043 2099 9 0.010152628 97.08394
2044 2100 8 0.009024558 97.09296
2045 2101 6 0.006768419 97.09973
2046 2102 2 0.002256140 97.10199
2047 2103 4 0.004512279 97.10650
2048 2105 1 0.001128070 97.10763
2049 2107 3 0.003384209 97.11101
2050 2108 1 0.001128070 97.11214
2051 2109 4 0.004512279 97.11665
2052 2110 2 0.002256140 97.11891
2053 2111 3 0.003384209 97.12229
2054 2112 1 0.001128070 97.12342
2055 2113 2 0.002256140 97.12568
2056 2114 1 0.001128070 97.12681
2057 2115 1 0.001128070 97.12793
2058 2117 1 0.001128070 97.12906
2059 2121 2 0.002256140 97.13132
2060 2122 2 0.002256140 97.13357
2061 2123 13 0.014664907 97.14824
2062 2125 3 0.003384209 97.15162
2063 2126 6 0.006768419 97.15839
2064 2127 1 0.001128070 97.15952
2065 2128 3 0.003384209 97.16290
2066 2131 1 0.001128070 97.16403
2067 2132 2 0.002256140 97.16629
2068 2133 8 0.009024558 97.17531
2069 2136 1 0.001128070 97.17644
2070 2137 1 0.001128070 97.17757
2071 2138 1 0.001128070 97.17870
2072 2139 2 0.002256140 97.18095
2073 2140 1 0.001128070 97.18208
2074 2141 1 0.001128070 97.18321
2075 2142 2 0.002256140 97.18547
2076 2143 1 0.001128070 97.18659
2077 2145 2 0.002256140 97.18885
2078 2146 1 0.001128070 97.18998
2079 2147 1 0.001128070 97.19111
2080 2151 2 0.002256140 97.19336
2081 2152 1 0.001128070 97.19449
2082 2153 2 0.002256140 97.19675
2083 2154 7 0.007896488 97.20464
2084 2155 10 0.011280698 97.21592
2085 2156 8 0.009024558 97.22495
2086 2157 3 0.003384209 97.22833
2087 2159 2 0.002256140 97.23059
2088 2160 1 0.001128070 97.23172
2089 2161 2 0.002256140 97.23397
2090 2164 4 0.004512279 97.23849
2091 2165 1 0.001128070 97.23961
2092 2166 1 0.001128070 97.24074
2093 2167 1 0.001128070 97.24187
2094 2168 2 0.002256140 97.24413
2095 2169 4 0.004512279 97.24864
2096 2171 6 0.006768419 97.25541
2097 2173 1 0.001128070 97.25653
2098 2174 1 0.001128070 97.25766
2099 2175 2 0.002256140 97.25992
2100 2177 1 0.001128070 97.26105
2101 2178 4 0.004512279 97.26556
2102 2179 4 0.004512279 97.27007
2103 2180 1 0.001128070 97.27120
2104 2181 1 0.001128070 97.27233
2105 2183 5 0.005640349 97.27797
2106 2184 2 0.002256140 97.28022
2107 2185 9 0.010152628 97.29038
2108 2186 4 0.004512279 97.29489
2109 2187 2 0.002256140 97.29714
2110 2189 3 0.003384209 97.30053
2111 2190 3 0.003384209 97.30391
2112 2191 6 0.006768419 97.31068
2113 2194 3 0.003384209 97.31407
2114 2195 1 0.001128070 97.31519
2115 2197 2 0.002256140 97.31745
2116 2200 1 0.001128070 97.31858
2117 2201 1 0.001128070 97.31971
2118 2203 1 0.001128070 97.32083
2119 2204 3 0.003384209 97.32422
2120 2208 1 0.001128070 97.32535
2121 2209 2 0.002256140 97.32760
2122 2210 3 0.003384209 97.33099
2123 2211 2 0.002256140 97.33324
2124 2212 2 0.002256140 97.33550
2125 2213 1 0.001128070 97.33663
2126 2214 1 0.001128070 97.33776
2127 2215 2 0.002256140 97.34001
2128 2216 1 0.001128070 97.34114
2129 2219 1 0.001128070 97.34227
2130 2220 2 0.002256140 97.34452
2131 2222 2 0.002256140 97.34678
2132 2223 1 0.001128070 97.34791
2133 2224 1 0.001128070 97.34904
2134 2225 1 0.001128070 97.35016
2135 2226 1 0.001128070 97.35129
2136 2228 3 0.003384209 97.35468
2137 2229 3 0.003384209 97.35806
2138 2231 3 0.003384209 97.36144
2139 2233 2 0.002256140 97.36370
2140 2234 1 0.001128070 97.36483
2141 2235 3 0.003384209 97.36821
2142 2236 1 0.001128070 97.36934
2143 2237 3 0.003384209 97.37273
2144 2238 1 0.001128070 97.37385
2145 2239 1 0.001128070 97.37498
2146 2240 4 0.004512279 97.37949
2147 2241 1 0.001128070 97.38062
2148 2244 1 0.001128070 97.38175
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2640 2946 1 0.001128070 98.48726
2641 2947 1 0.001128070 98.48839
2642 2949 1 0.001128070 98.48951
2643 2950 1 0.001128070 98.49064
2644 2953 1 0.001128070 98.49177
2645 2954 1 0.001128070 98.49290
2646 2956 1 0.001128070 98.49403
2647 2958 1 0.001128070 98.49515
2648 2961 2 0.002256140 98.49741
2649 2963 1 0.001128070 98.49854
2650 2965 1 0.001128070 98.49967
2651 2966 2 0.002256140 98.50192
2652 2968 2 0.002256140 98.50418
2653 2969 2 0.002256140 98.50644
2654 2973 2 0.002256140 98.50869
2655 2974 4 0.004512279 98.51320
2656 2975 1 0.001128070 98.51433
2657 2976 2 0.002256140 98.51659
2658 2977 2 0.002256140 98.51884
2659 2978 1 0.001128070 98.51997
2660 2979 1 0.001128070 98.52110
2661 2981 1 0.001128070 98.52223
2662 2982 1 0.001128070 98.52336
2663 2984 2 0.002256140 98.52561
2664 2985 4 0.004512279 98.53013
2665 2986 1 0.001128070 98.53125
2666 2987 1 0.001128070 98.53238
2667 2988 1 0.001128070 98.53351
2668 2989 2 0.002256140 98.53577
2669 2990 1 0.001128070 98.53689
2670 2991 2 0.002256140 98.53915
2671 2992 1 0.001128070 98.54028
2672 2994 1 0.001128070 98.54141
2673 2996 1 0.001128070 98.54253
2674 2997 1 0.001128070 98.54366
2675 2998 2 0.002256140 98.54592
2676 3000 2 0.002256140 98.54817
2677 3002 1 0.001128070 98.54930
2678 3003 1 0.001128070 98.55043
2679 3013 1 0.001128070 98.55156
2680 3018 1 0.001128070 98.55269
2681 3019 1 0.001128070 98.55381
2682 3020 1 0.001128070 98.55494
2683 3021 6 0.006768419 98.56171
2684 3023 2 0.002256140 98.56397
2685 3027 2 0.002256140 98.56622
2686 3029 1 0.001128070 98.56735
2687 3030 1 0.001128070 98.56848
2688 3032 1 0.001128070 98.56961
2689 3033 1 0.001128070 98.57074
2690 3035 1 0.001128070 98.57186
2691 3041 5 0.005640349 98.57750
2692 3042 2 0.002256140 98.57976
2693 3043 2 0.002256140 98.58202
2694 3044 1 0.001128070 98.58314
2695 3049 1 0.001128070 98.58427
2696 3050 3 0.003384209 98.58766
2697 3051 2 0.002256140 98.58991
2698 3052 2 0.002256140 98.59217
2699 3053 2 0.002256140 98.59443
2700 3054 1 0.001128070 98.59555
2701 3058 2 0.002256140 98.59781
2702 3059 1 0.001128070 98.59894
2703 3060 2 0.002256140 98.60119
2704 3061 2 0.002256140 98.60345
2705 3063 1 0.001128070 98.60458
2706 3065 2 0.002256140 98.60683
2707 3067 1 0.001128070 98.60796
2708 3069 5 0.005640349 98.61360
2709 3070 2 0.002256140 98.61586
2710 3071 3 0.003384209 98.61924
2711 3072 1 0.001128070 98.62037
2712 3073 1 0.001128070 98.62150
2713 3075 3 0.003384209 98.62488
2714 3077 1 0.001128070 98.62601
2715 3078 1 0.001128070 98.62714
2716 3079 1 0.001128070 98.62827
2717 3088 1 0.001128070 98.62940
2718 3089 1 0.001128070 98.63052
2719 3091 2 0.002256140 98.63278
2720 3093 3 0.003384209 98.63616
2721 3094 2 0.002256140 98.63842
2722 3098 1 0.001128070 98.63955
2723 3099 2 0.002256140 98.64180
2724 3101 1 0.001128070 98.64293
2725 3103 1 0.001128070 98.64406
2726 3104 2 0.002256140 98.64632
2727 3105 2 0.002256140 98.64857
2728 3106 3 0.003384209 98.65196
2729 3108 1 0.001128070 98.65308
2730 3109 2 0.002256140 98.65534
2731 3113 2 0.002256140 98.65760
2732 3114 2 0.002256140 98.65985
2733 3115 2 0.002256140 98.66211
2734 3118 1 0.001128070 98.66324
2735 3119 4 0.004512279 98.66775
2736 3121 1 0.001128070 98.66888
2737 3123 1 0.001128070 98.67001
2738 3125 2 0.002256140 98.67226
2739 3128 1 0.001128070 98.67339
2740 3129 1 0.001128070 98.67452
2741 3131 2 0.002256140 98.67677
2742 3132 3 0.003384209 98.68016
2743 3133 1 0.001128070 98.68129
2744 3134 1 0.001128070 98.68241
2745 3136 3 0.003384209 98.68580
2746 3137 6 0.006768419 98.69257
2747 3138 1 0.001128070 98.69370
2748 3139 1 0.001128070 98.69482
2749 3141 1 0.001128070 98.69595
2750 3143 3 0.003384209 98.69934
2751 3144 1 0.001128070 98.70046
2752 3149 2 0.002256140 98.70272
2753 3154 1 0.001128070 98.70385
2754 3155 2 0.002256140 98.70610
2755 3157 1 0.001128070 98.70723
2756 3159 26 0.029329814 98.73656
2757 3160 4 0.004512279 98.74107
2758 3164 2 0.002256140 98.74333
2759 3165 5 0.005640349 98.74897
2760 3167 2 0.002256140 98.75123
2761 3168 2 0.002256140 98.75348
2762 3169 1 0.001128070 98.75461
2763 3170 1 0.001128070 98.75574
2764 3171 1 0.001128070 98.75687
2765 3172 2 0.002256140 98.75912
2766 3173 1 0.001128070 98.76025
2767 3174 1 0.001128070 98.76138
2768 3175 1 0.001128070 98.76251
2769 3176 1 0.001128070 98.76364
2770 3177 1 0.001128070 98.76476
2771 3179 2 0.002256140 98.76702
2772 3180 1 0.001128070 98.76815
2773 3184 1 0.001128070 98.76928
2774 3185 1 0.001128070 98.77040
2775 3186 1 0.001128070 98.77153
2776 3191 1 0.001128070 98.77266
2777 3192 2 0.002256140 98.77492
2778 3193 1 0.001128070 98.77604
2779 3199 1 0.001128070 98.77717
2780 3201 4 0.004512279 98.78168
2781 3205 1 0.001128070 98.78281
2782 3206 2 0.002256140 98.78507
2783 3208 1 0.001128070 98.78620
2784 3211 2 0.002256140 98.78845
2785 3212 3 0.003384209 98.79184
2786 3214 2 0.002256140 98.79409
2787 3217 1 0.001128070 98.79522
2788 3218 2 0.002256140 98.79748
2789 3219 1 0.001128070 98.79861
2790 3225 1 0.001128070 98.79973
2791 3227 1 0.001128070 98.80086
2792 3228 1 0.001128070 98.80199
2793 3229 1 0.001128070 98.80312
2794 3231 2 0.002256140 98.80537
2795 3233 1 0.001128070 98.80650
2796 3235 1 0.001128070 98.80763
2797 3236 1 0.001128070 98.80876
2798 3238 3 0.003384209 98.81214
2799 3239 1 0.001128070 98.81327
2800 3240 1 0.001128070 98.81440
2801 3242 1 0.001128070 98.81553
2802 3248 4 0.004512279 98.82004
2803 3251 1 0.001128070 98.82117
2804 3252 2 0.002256140 98.82342
2805 3253 1 0.001128070 98.82455
2806 3254 1 0.001128070 98.82568
2807 3255 2 0.002256140 98.82794
2808 3256 1 0.001128070 98.82906
2809 3260 1 0.001128070 98.83019
2810 3261 1 0.001128070 98.83132
2811 3263 1 0.001128070 98.83245
2812 3268 3 0.003384209 98.83583
2813 3269 1 0.001128070 98.83696
2814 3271 5 0.005640349 98.84260
2815 3272 1 0.001128070 98.84373
2816 3275 1 0.001128070 98.84486
2817 3277 1 0.001128070 98.84598
2818 3278 1 0.001128070 98.84711
2819 3279 1 0.001128070 98.84824
2820 3281 2 0.002256140 98.85050
2821 3284 1 0.001128070 98.85162
2822 3286 3 0.003384209 98.85501
2823 3287 1 0.001128070 98.85614
2824 3290 2 0.002256140 98.85839
2825 3291 1 0.001128070 98.85952
2826 3293 1 0.001128070 98.86065
2827 3294 6 0.006768419 98.86742
2828 3295 1 0.001128070 98.86855
2829 3297 2 0.002256140 98.87080
2830 3298 3 0.003384209 98.87419
2831 3299 2 0.002256140 98.87644
2832 3300 3 0.003384209 98.87983
2833 3301 1 0.001128070 98.88095
2834 3302 4 0.004512279 98.88547
2835 3304 3 0.003384209 98.88885
2836 3306 1 0.001128070 98.88998
2837 3308 2 0.002256140 98.89224
2838 3310 1 0.001128070 98.89336
2839 3312 4 0.004512279 98.89788
2840 3313 3 0.003384209 98.90126
2841 3314 1 0.001128070 98.90239
2842 3317 4 0.004512279 98.90690
2843 3318 5 0.005640349 98.91254
2844 3319 2 0.002256140 98.91480
2845 3320 1 0.001128070 98.91592
2846 3321 1 0.001128070 98.91705
2847 3322 1 0.001128070 98.91818
2848 3325 2 0.002256140 98.92044
2849 3331 2 0.002256140 98.92269
2850 3332 3 0.003384209 98.92608
2851 3333 1 0.001128070 98.92721
2852 3334 1 0.001128070 98.92833
2853 3335 1 0.001128070 98.92946
2854 3336 1 0.001128070 98.93059
2855 3337 1 0.001128070 98.93172
2856 3338 2 0.002256140 98.93397
2857 3339 2 0.002256140 98.93623
2858 3343 2 0.002256140 98.93849
2859 3344 1 0.001128070 98.93961
2860 3345 2 0.002256140 98.94187
2861 3346 3 0.003384209 98.94525
2862 3347 1 0.001128070 98.94638
2863 3348 3 0.003384209 98.94977
2864 3350 1 0.001128070 98.95090
2865 3353 2 0.002256140 98.95315
2866 3355 3 0.003384209 98.95654
2867 3357 1 0.001128070 98.95766
2868 3359 1 0.001128070 98.95879
2869 3360 7 0.007896488 98.96669
2870 3361 4 0.004512279 98.97120
2871 3362 4 0.004512279 98.97571
2872 3363 2 0.002256140 98.97797
2873 3364 1 0.001128070 98.97910
2874 3365 1 0.001128070 98.98022
2875 3366 1 0.001128070 98.98135
2876 3367 1 0.001128070 98.98248
2877 3369 2 0.002256140 98.98474
2878 3374 1 0.001128070 98.98587
2879 3376 2 0.002256140 98.98812
2880 3377 15 0.016921046 99.00504
2881 3378 13 0.014664907 99.01971
2882 3379 5 0.005640349 99.02535
2883 3380 2 0.002256140 99.02760
2884 3381 1 0.001128070 99.02873
2885 3382 1 0.001128070 99.02986
2886 3383 1 0.001128070 99.03099
2887 3386 2 0.002256140 99.03324
2888 3387 2 0.002256140 99.03550
2889 3388 1 0.001128070 99.03663
2890 3389 3 0.003384209 99.04001
2891 3390 2 0.002256140 99.04227
2892 3392 1 0.001128070 99.04340
2893 3394 2 0.002256140 99.04565
2894 3396 2 0.002256140 99.04791
2895 3397 1 0.001128070 99.04904
2896 3398 8 0.009024558 99.05806
2897 3399 6 0.006768419 99.06483
2898 3400 2 0.002256140 99.06709
2899 3401 1 0.001128070 99.06821
2900 3402 2 0.002256140 99.07047
2901 3403 4 0.004512279 99.07498
2902 3405 2 0.002256140 99.07724
2903 3406 1 0.001128070 99.07837
2904 3409 3 0.003384209 99.08175
2905 3410 2 0.002256140 99.08401
2906 3411 2 0.002256140 99.08626
2907 3412 5 0.005640349 99.09190
2908 3413 3 0.003384209 99.09529
2909 3414 1 0.001128070 99.09642
2910 3418 1 0.001128070 99.09754
2911 3420 1 0.001128070 99.09867
2912 3421 3 0.003384209 99.10206
2913 3422 2 0.002256140 99.10431
2914 3423 1 0.001128070 99.10544
2915 3425 4 0.004512279 99.10995
2916 3428 2 0.002256140 99.11221
2917 3429 1 0.001128070 99.11334
2918 3430 1 0.001128070 99.11447
2919 3431 3 0.003384209 99.11785
2920 3432 8 0.009024558 99.12687
2921 3434 2 0.002256140 99.12913
2922 3435 4 0.004512279 99.13364
2923 3437 3 0.003384209 99.13703
2924 3438 2 0.002256140 99.13928
2925 3439 1 0.001128070 99.14041
2926 3442 2 0.002256140 99.14267
2927 3443 4 0.004512279 99.14718
2928 3444 8 0.009024558 99.15620
2929 3445 5 0.005640349 99.16184
2930 3446 4 0.004512279 99.16636
2931 3449 1 0.001128070 99.16748
2932 3450 1 0.001128070 99.16861
2933 3451 3 0.003384209 99.17200
2934 3452 2 0.002256140 99.17425
2935 3453 1 0.001128070 99.17538
2936 3454 1 0.001128070 99.17651
2937 3456 1 0.001128070 99.17764
2938 3457 1 0.001128070 99.17877
2939 3458 2 0.002256140 99.18102
2940 3459 4 0.004512279 99.18553
2941 3460 1 0.001128070 99.18666
2942 3461 1 0.001128070 99.18779
2943 3462 4 0.004512279 99.19230
2944 3463 118 0.133112232 99.32541
2945 3464 164 0.185003441 99.51042
2946 3465 63 0.071068395 99.58149
2947 3466 1 0.001128070 99.58261
2948 3467 2 0.002256140 99.58487
2949 3468 6 0.006768419 99.59164
2950 3469 9 0.010152628 99.60179
2951 3470 19 0.021433325 99.62322
2952 3471 9 0.010152628 99.63338
2953 3472 5 0.005640349 99.63902
2954 3473 4 0.004512279 99.64353
2955 3474 4 0.004512279 99.64804
2956 3476 3 0.003384209 99.65143
2957 3477 1 0.001128070 99.65255
2958 3478 3 0.003384209 99.65594
2959 3479 5 0.005640349 99.66158
2960 3480 4 0.004512279 99.66609
2961 3481 1 0.001128070 99.66722
2962 3482 1 0.001128070 99.66835
2963 3483 3 0.003384209 99.67173
2964 3485 15 0.016921046 99.68865
2965 3486 4 0.004512279 99.69317
2966 3487 6 0.006768419 99.69993
2967 3488 2 0.002256140 99.70219
2968 3489 2 0.002256140 99.70445
2969 3491 1 0.001128070 99.70557
2970 3492 3 0.003384209 99.70896
2971 3493 2 0.002256140 99.71121
2972 3494 4 0.004512279 99.71573
2973 3495 9 0.010152628 99.72588
2974 3497 2 0.002256140 99.72814
2975 3499 1 0.001128070 99.72926
2976 3502 6 0.006768419 99.73603
2977 3504 1 0.001128070 99.73716
2978 3505 1 0.001128070 99.73829
2979 3507 2 0.002256140 99.74054
2980 3508 2 0.002256140 99.74280
2981 3509 2 0.002256140 99.74506
2982 3510 3 0.003384209 99.74844
2983 3511 3 0.003384209 99.75182
2984 3512 3 0.003384209 99.75521
2985 3513 1 0.001128070 99.75634
2986 3516 3 0.003384209 99.75972
2987 3517 1 0.001128070 99.76085
2988 3518 1 0.001128070 99.76198
2989 3521 2 0.002256140 99.76423
2990 3522 1 0.001128070 99.76536
2991 3523 4 0.004512279 99.76987
2992 3524 3 0.003384209 99.77326
2993 3525 3 0.003384209 99.77664
2994 3526 1 0.001128070 99.77777
2995 3527 1 0.001128070 99.77890
2996 3529 1 0.001128070 99.78003
2997 3531 1 0.001128070 99.78115
2998 3532 1 0.001128070 99.78228
2999 3534 2 0.002256140 99.78454
3000 3536 2 0.002256140 99.78679
3001 3537 2 0.002256140 99.78905
3002 3538 1 0.001128070 99.79018
3003 3539 1 0.001128070 99.79131
3004 3540 3 0.003384209 99.79469
3005 3542 1 0.001128070 99.79582
3006 3544 2 0.002256140 99.79808
3007 3546 2 0.002256140 99.80033
3008 3547 1 0.001128070 99.80146
3009 3548 1 0.001128070 99.80259
3010 3549 1 0.001128070 99.80372
3011 3551 2 0.002256140 99.80597
3012 3552 2 0.002256140 99.80823
3013 3553 1 0.001128070 99.80936
3014 3555 1 0.001128070 99.81048
3015 3557 3 0.003384209 99.81387
3016 3558 4 0.004512279 99.81838
3017 3559 4 0.004512279 99.82289
3018 3561 2 0.002256140 99.82515
3019 3563 3 0.003384209 99.82853
3020 3564 1 0.001128070 99.82966
3021 3565 2 0.002256140 99.83192
3022 3566 3 0.003384209 99.83530
3023 3567 2 0.002256140 99.83756
3024 3568 2 0.002256140 99.83981
3025 3569 1 0.001128070 99.84094
3026 3572 1 0.001128070 99.84207
3027 3573 2 0.002256140 99.84433
3028 3575 2 0.002256140 99.84658
3029 3576 1 0.001128070 99.84771
3030 3577 1 0.001128070 99.84884
3031 3578 1 0.001128070 99.84997
3032 3579 4 0.004512279 99.85448
3033 3580 1 0.001128070 99.85561
3034 3581 2 0.002256140 99.85786
3035 3584 1 0.001128070 99.85899
3036 3585 1 0.001128070 99.86012
3037 3586 1 0.001128070 99.86125
3038 3587 4 0.004512279 99.86576
3039 3588 1 0.001128070 99.86689
3040 3589 6 0.006768419 99.87366
3041 3590 3 0.003384209 99.87704
3042 3591 2 0.002256140 99.87930
3043 3592 2 0.002256140 99.88155
3044 3595 1 0.001128070 99.88268
3045 3596 2 0.002256140 99.88494
3046 3598 5 0.005640349 99.89058
3047 3599 2 0.002256140 99.89283
3048 3600 3 0.003384209 99.89622
3049 3604 1 0.001128070 99.89735
3050 3605 3 0.003384209 99.90073
3051 3606 5 0.005640349 99.90637
3052 3607 3 0.003384209 99.90975
3053 3608 1 0.001128070 99.91088
3054 3609 2 0.002256140 99.91314
3055 3612 1 0.001128070 99.91427
3056 3613 1 0.001128070 99.91539
3057 3614 2 0.002256140 99.91765
3058 3615 1 0.001128070 99.91878
3059 3618 2 0.002256140 99.92104
3060 3621 4 0.004512279 99.92555
3061 3622 1 0.001128070 99.92668
3062 3624 3 0.003384209 99.93006
3063 3625 2 0.002256140 99.93232
3064 3626 1 0.001128070 99.93344
3065 3627 1 0.001128070 99.93457
3066 3629 1 0.001128070 99.93570
3067 3630 1 0.001128070 99.93683
3068 3631 2 0.002256140 99.93908
3069 3633 2 0.002256140 99.94134
3070 3634 2 0.002256140 99.94360
3071 3635 7 0.007896488 99.95149
3072 3636 3 0.003384209 99.95488
3073 3637 2 0.002256140 99.95713
3074 3638 2 0.002256140 99.95939
3075 3639 3 0.003384209 99.96277
3076 3640 4 0.004512279 99.96729
3077 3642 3 0.003384209 99.97067
3078 3643 3 0.003384209 99.97405
3079 3644 1 0.001128070 99.97518
3080 3645 2 0.002256140 99.97744
3081 3646 2 0.002256140 99.97969
3082 3648 1 0.001128070 99.98082
3083 3650 2 0.002256140 99.98308
3084 3651 3 0.003384209 99.98646
3085 3658 1 0.001128070 99.98759
3086 3668 1 0.001128070 99.98872
3087 3682 2 0.002256140 99.99098
3088 3688 1 0.001128070 99.99210
3089 3690 2 0.002256140 99.99436
3090 3713 2 0.002256140 99.99662
3091 3716 1 0.001128070 99.99774
3092 3718 1 0.001128070 99.99887
3093 22574 1 0.001128070 100.00000
phishing_bruto |> group_by(time_domain_expiration) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
# A tibble: 4,348 × 4
# Groups: time_domain_expiration [3,093]
time_domain_expiration phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 14928 57.0
2 -1 1 11282 43.0
3 0 0 25 37.9
4 0 1 41 62.1
5 1 0 56 50.5
6 1 1 55 49.5
7 2 0 36 48
8 2 1 39 52
9 3 0 31 45.6
10 3 1 37 54.4
# … with 4,338 more rows
min max
1 -1 22574
Vemos que volvemos a tener el valor -1, el cual nos indica que la característica no es aplicable en aquellas observaciones que lo presentan. Por otra parte, observamos un valor máximo más elevado que el anterior.
phishing_bruto |> summarise(mean = mean(time_domain_expiration),
median = median(time_domain_expiration))
mean median
1 352.0433 168
Vemos que los valores de la media y mediana son muy distintos, lo que nos da a intuír que tendremos una distribución considerablemente asimétrica, donde la media será muy poco representativa de nuestros datos.
# Visualización
ggplot(phishing_bruto, aes(x = time_domain_expiration)) +
geom_density(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por tiempo de la expriación del dominio",
x = "Tiempo de la expiración del dominio (en días)", y = "Densidad", fill = "Phishing") +
scale_x_continuous(breaks = seq(0, 20000, 1500)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Parece que a partir del 3500 aproxiamdamente, apenas podemos ver observaciones
phishing_bruto |> filter(time_domain_expiration >= 3500) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
phishing n porc
1 0 211 87.91667
2 1 29 12.08333
Y como vemos, a parte de tener muy pocos registros, las proporciones indican que casi todos los registros, como pasaba también en la variable anterior, se corresponden a webs legítimas. Por lo tanto, usando una lógica similar, imputaremos el valor 3500 a aquellos registros que tengan uno más elevado en función de esta variable, para de esta forma, simplificar en gran medida los valores que puede tomar el tiempo de expiración del dominio.
qty_dot_directory count (.) in directory qty_hyphen_directory count (-) in directory qty_underline_directory count (_) in directory qty_slash_directory count (/) in directory qty_questionmark_directory count (?) in directory qty_equal_directory count (=) in directory qty_at_directory count (@) in directory qty_and_directory count (&) in directory qty_exclamation_directory count (!) in directory qty_space_directory count ( ) in directory qty_tilde_directory count (~) in directory qty_comma_directory count (,) in directory qty_plus_directory count (+) in directory qty_asterisk_directory count (*) in directory qty_hashtag_directory count (#) in directory qty_dollar_directory count ($) in directory qty_percent_directory count (%) in directory directory_length directory length
Comprobamos el número de valores únicos en estas variables:
phishing_bruto |> select(contains("directory")) |>
summarise_all(list(~n_distinct(.)))
qty_dot_directory qty_hyphen_directory qty_underline_directory
1 16 24 18
qty_slash_directory qty_questionmark_directory qty_equal_directory
1 20 2 7
qty_at_directory qty_and_directory qty_exclamation_directory
1 13 14 9
qty_space_directory qty_tilde_directory qty_comma_directory
1 9 7 5
qty_plus_directory qty_asterisk_directory qty_hashtag_directory
1 10 16 2
qty_dollar_directory qty_percent_directory directory_length
1 11 43 239
Convertimos a factor para una mejor visualización:
phishing_bruto <- phishing_bruto |> mutate(qty_dot_directory = as_factor(qty_dot_directory),
qty_hyphen_directory = as_factor(qty_hyphen_directory),
qty_underline_directory = as_factor(qty_underline_directory),
qty_slash_directory = as_factor(qty_slash_directory),
qty_questionmark_directory = as_factor(qty_questionmark_directory),
qty_equal_directory = as_factor(qty_equal_directory),
qty_at_directory = as_factor(qty_at_directory),
qty_and_directory = as_factor(qty_and_directory),
qty_exclamation_directory = as_factor(qty_exclamation_directory),
qty_space_directory = as_factor(qty_space_directory),
qty_tilde_directory = as_factor(qty_tilde_directory),
qty_comma_directory =as_factor(qty_comma_directory),
qty_plus_directory = as_factor(qty_plus_directory),
qty_asterisk_directory = as_factor(qty_asterisk_directory),
qty_hashtag_directory = as_factor(qty_hashtag_directory),
qty_dollar_directory = as_factor(qty_dollar_directory))
En este caso, en función de la cantidad de valores únicos y para una mejor visualización, no convertimos en factor las variables de directorio: qty_percent_directory y directory_length.
Análisis cuantitativo:
# qty_dot_directory
phishing_bruto |> group_by(qty_dot_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 23 × 4
# Groups: qty_dot_directory [16]
qty_dot_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 9522 36.8
4 0 1 16372 63.2
5 1 0 1654 12.4
6 1 1 11653 87.6
7 2 0 44 3.85
8 2 1 1098 96.1
9 3 0 14 3.38
10 3 1 400 96.6
# … with 13 more rows
# qty_hyphen_directory
phishing_bruto |> group_by(qty_hyphen_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 45 × 4
# Groups: qty_hyphen_directory [24]
qty_hyphen_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 10134 30.1
4 0 1 23532 69.9
5 1 0 296 5.98
6 1 1 4650 94.0
7 2 0 173 16.8
8 2 1 858 83.2
9 3 0 109 25.5
10 3 1 319 74.5
# … with 35 more rows
# qty_underline_directory
phishing_bruto |> group_by(qty_underline_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 32 × 4
# Groups: qty_underline_directory [18]
qty_underline_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 10675 28.2
4 0 1 27193 71.8
5 1 0 313 13.7
6 1 1 1971 86.3
7 2 0 116 18.9
8 2 1 499 81.1
9 3 0 69 35.2
10 3 1 127 64.8
# … with 22 more rows
# qty_slash_directory
phishing_bruto |> group_by(qty_slash_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 31 × 4
# Groups: qty_slash_directory [20]
qty_slash_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 1 0 7123 51.4
4 1 1 6735 48.6
5 2 0 2258 27.7
6 2 1 5895 72.3
7 3 0 1216 14.5
8 3 1 7172 85.5
9 4 0 283 6.11
10 4 1 4348 93.9
# … with 21 more rows
# qty_questionmark_directory
phishing_bruto |> group_by(qty_questionmark_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_questionmark_directory [2]
qty_questionmark_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
Como podemos ver, antes de empezar a observar la distribución de nuestra objetivo en estas variables, volvemos a tener niveles negativos en variables representativas de cantidades de caracteres. Una vez más, podemos interpretar dichos niveles de forma que refleja que dicha característica no se aplica en los directorios o no se puede determinar para esos directorios en cuestión. Y por otra parte, cuando es 0, supondremos que la característica está presente pero en este caso no se encuentra presente en el enlance en cuestión.
Visualización:
# qty_dot_directory
ggplot(phishing_bruto, aes(x = qty_dot_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (.) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hyphen_directory
ggplot(phishing_bruto, aes(x = qty_hyphen_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (-) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_underline_directory
ggplot(phishing_bruto, aes(x = qty_underline_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (_) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_slash_directory
ggplot(phishing_bruto, aes(x = qty_slash_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (/) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_questionmark_directory
ggplot(phishing_bruto, aes(x = qty_questionmark_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (?) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
De forma general podemos ver en un principio, que el nivel más frecuente en este grupo de variables es aquel que indica que dicha característica no se aplica en dichas observaciones.
Conclusiones:
qty_dot_directory: Vemos que cuando no se aplica la característica, la
proporción de webs legítimas es cercana al 100%. Sin embargo, cuando la
caractrística puede estar presente pero la cantidad de puntos en directorio es
0, el porcentaje de webs de phishing es ligeramente superior al de legítimas
y considerablemente mayor si lo comparamos con las proporciones globales.
A partir del 0, vemos como a parte de que no disponemos de muchos datos en la
mayoría de niveles, la probabilidad de que la página web sea de phishing es
cercana o superior al 90%. De este modo, una posible tranformación para esta
variable podría ser una recategorización en tres niveles: -1, 0, >0.qty_hyphen_directory: En este caso, la objetivo se comporta de forma similar
a nuestra nterior variable. No obstante, sí podemos observar en niveles donde
no contamos con demasiados datos, las proporciones de webs de phishing y
legítimas presentan más variedad que en la variable anterior. Es decir, podemos
ver que cuando la cantidad de puntos en directorio es mayor que 9, hay una mayor
presencia de webs legítimas, y en cambio cuando dicha cantidad está entre 2 y 4,
predominan las webs de phishing. Podríamos considerar en rescatar información
relvante de forma que diferenciemos un mayor número de niveles en base a sus
proporciones, pero ya que contamos con cantidades muy pequeñas de observaciones
en la mayoría de ellos, vamos a aplicar una recategorización similar a la anterior
y de esta forma también, evitar el sobreajuste: -1, 0, >0.qty_underline_directory: Sucede exactamente lo mismo que en el caso de la
variable cantidad de guiones: -1, 0, >0.qty_slash_directory: Podemos observar que a partir de una cantidad de guiones
bajos de 1, la probabilidad de encontrarnos con una web de phishing es muy elevada,
superior al 70%. Cuando esta es de 1, vemos que la proporciones son casi iguales,
aunque también nos aportaría información relevante para nuestro modelo, ya que
las webs de phishing son más frecuentes comparandolo con el conjunto de datos.
Por último también tenemos el nivel que indica que no se aplica esta característica,
el cual una vez más contiene casi el 100% de webs legítimas en sus proporciones:
-1, 1, >1.qty_questionmark_directory: Tenemos dos niveles con proporciones diferenciales
en nuestra objetivo. La dejaremos como está.Análisis cuantitativo:
# qty_equal_directory
phishing_bruto |> group_by(qty_equal_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 10 × 4
# Groups: qty_equal_directory [7]
qty_equal_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11237 27.7
4 0 1 29358 72.3
5 1 1 446 100
6 2 1 73 100
7 3 0 1 5.88
8 3 1 16 94.1
9 4 1 5 100
10 5 1 2 100
# qty_at_directory
phishing_bruto |> group_by(qty_at_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 16 × 4
# Groups: qty_at_directory [13]
qty_at_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11236 27.4
4 0 1 29774 72.6
5 1 0 2 1.96
6 1 1 100 98.0
7 2 1 7 100
8 3 1 6 100
9 4 1 4 100
10 5 1 2 100
11 6 1 1 100
12 10 1 1 100
13 11 1 1 100
14 12 1 1 100
15 15 1 1 100
16 43 1 2 100
# qty_and_directory
phishing_bruto |> group_by(qty_and_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 18 × 4
# Groups: qty_and_directory [14]
qty_and_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11236 27.5
4 0 1 29685 72.5
5 1 0 1 0.529
6 1 1 188 99.5
7 2 0 1 14.3
8 2 1 6 85.7
9 3 1 5 100
10 4 1 3 100
11 5 1 3 100
12 7 1 2 100
13 11 1 1 100
14 12 1 1 100
15 13 1 1 100
16 17 1 2 100
17 19 1 1 100
18 26 1 2 100
# qty_exclamation_directory
phishing_bruto |> group_by(qty_exclamation_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 11 × 4
# Groups: qty_exclamation_directory [9]
qty_exclamation_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.4
4 0 1 29790 72.6
5 1 1 96 100
6 2 1 8 100
7 3 1 1 100
8 4 1 1 100
9 5 1 2 100
10 6 1 1 100
11 9 1 1 100
Visualización:
# qty_equal_directory
ggplot(phishing_bruto, aes(x = qty_equal_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (=) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_at_directory
ggplot(phishing_bruto, aes(x = qty_at_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (@) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_and_directory
ggplot(phishing_bruto, aes(x = qty_and_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (&) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_exclamation_directory
ggplot(phishing_bruto, aes(x = qty_exclamation_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (!) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
qty_equal_directory: Vemos que cuando no se aplica la característica, una
vez más, la proporción de webs legítimas casi cubre el 100% de los registros en
función de la cantidad de “=” en el directorio. Por otro lado, cuando sí se
aplica y la cantidad es 0, las proporciones se asemejan más a las globales (27%
de phishing). Por último, en niveles superiores a 0, tenemos una cantidad muy
pequeña de datos donde la proporción de phishing es prácticamente del 100%. Como
en este caso, el número de datos apenas llega al 1% del total, no sería conveniente
diferenciar un tercer nivel para aquellos superiores 0 en caso de recategorizar.
Priorizamos la simplicidad, y en caso de aplicarle una transformación a esta
variable, la dejaríamos como binaria de la siguiente forma: -1, >=0.qty_at_directory: Sucede exactamente lo mismo que en la variable anterior:
-1, >=0.qty_and_directory: Nuevamente: -1, >=0.qty_exclamation_directory: Misma lógica: -1, >=0.Análisis cuantitativo:
# qty_space_directory
phishing_bruto |> group_by(qty_space_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 11 × 4
# Groups: qty_space_directory [9]
qty_space_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29876 72.7
5 1 1 13 100
6 2 1 2 100
7 3 1 3 100
8 4 1 1 100
9 6 1 1 100
10 7 1 1 100
11 9 1 3 100
# qty_tilde_directory
phishing_bruto |> group_by(qty_tilde_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 12 × 4
# Groups: qty_tilde_directory [7]
qty_tilde_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11224 27.4
4 0 1 29702 72.6
5 1 0 12 6.59
6 1 1 170 93.4
7 2 1 5 100
8 3 0 1 5.88
9 3 1 16 94.1
10 4 0 1 50
11 4 1 1 50
12 5 1 6 100
# qty_comma_directory
phishing_bruto |> group_by(qty_comma_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 9 × 4
# Groups: qty_comma_directory [5]
qty_comma_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11224 27.3
4 0 1 29879 72.7
5 1 0 4 18.2
6 1 1 18 81.8
7 3 0 10 83.3
8 3 1 2 16.7
9 5 1 1 100
# qty_plus_directory
phishing_bruto |> group_by(qty_plus_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 15 × 4
# Groups: qty_plus_directory [10]
qty_plus_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11228 27.3
4 0 1 29877 72.7
5 1 0 4 22.2
6 1 1 14 77.8
7 2 0 1 25
8 2 1 3 75
9 3 1 4 100
10 4 0 1 50
11 4 1 1 50
12 5 1 1 100
13 7 0 1 100
14 9 0 2 100
15 19 0 1 100
Visualización:
# qty_space_directory
ggplot(phishing_bruto, aes(x = qty_space_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ( ) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_tilde_directory
ggplot(phishing_bruto, aes(x = qty_tilde_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (~) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_comma_directory
ggplot(phishing_bruto, aes(x = qty_comma_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (,) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_plus_directory
ggplot(phishing_bruto, aes(x = qty_plus_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (+) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_space_directory: Volvemos a tener una distribución similar a las
variables del anterior grupo: -1, >=0.qty_tilde_directory: -1, >=0.qty_comma_directory: Tenemos un nivel (3) donde cambia el patrón anterior y
volvemos a tener un porcentaje mayor de webs legítimas (83%). Sin embargo, la
cantidad de datos es nuevamente muy pequeña por lo que priorizamos la simplicidad
y evitamos el sobreajuste en caso de una recategorización: -1, >=0.qty_plus_directory: Aplicamos la misma lógica: -1, >=0.Análisis cuantitativo:
# qty_asterisk_directory
phishing_bruto |> group_by(qty_asterisk_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 18 × 4
# Groups: qty_asterisk_directory [16]
qty_asterisk_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29853 72.7
5 1 1 16 100
6 2 1 11 100
7 4 1 4 100
8 5 1 1 100
9 6 1 1 100
10 7 1 1 100
11 9 1 1 100
12 10 1 1 100
13 12 1 1 100
14 14 1 3 100
15 19 1 1 100
16 20 1 3 100
17 22 1 2 100
18 60 1 1 100
# qty_hashtag_directory
phishing_bruto |> group_by(qty_hashtag_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_hashtag_directory [2]
qty_hashtag_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
# qty_dollar_directory
phishing_bruto |> group_by(qty_dollar_directory) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 13 × 4
# Groups: qty_dollar_directory [11]
qty_dollar_directory phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29871 72.7
5 1 1 10 100
6 2 1 3 100
7 3 1 2 100
8 4 1 3 100
9 5 1 3 100
10 6 1 2 100
11 8 1 2 100
12 9 1 1 100
13 10 1 3 100
# qty_percent_directory
phishing_bruto |> count(qty_percent_directory, sort = TRUE) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_percent_directory n porc cumul
1 -1 47509 53.593466220 53.59347
2 0 40280 45.438649926 99.03212
3 1 478 0.539217345 99.57133
4 2 107 0.120703464 99.69204
5 3 58 0.065428046 99.75747
6 5 40 0.045122790 99.80259
7 6 24 0.027073674 99.82966
8 4 22 0.024817535 99.85448
9 8 15 0.016921046 99.87140
10 16 14 0.015792977 99.88719
11 9 12 0.013536837 99.90073
12 28 10 0.011280698 99.91201
13 7 5 0.005640349 99.91765
14 10 5 0.005640349 99.92329
15 12 5 0.005640349 99.92893
16 18 5 0.005640349 99.93457
17 20 5 0.005640349 99.94021
18 22 5 0.005640349 99.94585
19 24 5 0.005640349 99.95149
20 32 5 0.005640349 99.95713
21 34 5 0.005640349 99.96277
22 15 4 0.004512279 99.96729
23 38 3 0.003384209 99.97067
24 14 2 0.002256140 99.97293
25 36 2 0.002256140 99.97518
26 44 2 0.002256140 99.97744
27 62 2 0.002256140 99.97969
28 66 2 0.002256140 99.98195
29 78 2 0.002256140 99.98421
30 13 1 0.001128070 99.98534
31 23 1 0.001128070 99.98646
32 27 1 0.001128070 99.98759
33 29 1 0.001128070 99.98872
34 40 1 0.001128070 99.98985
35 42 1 0.001128070 99.99098
36 46 1 0.001128070 99.99210
37 48 1 0.001128070 99.99323
38 51 1 0.001128070 99.99436
39 54 1 0.001128070 99.99549
40 117 1 0.001128070 99.99662
41 127 1 0.001128070 99.99774
42 162 1 0.001128070 99.99887
43 174 1 0.001128070 100.00000
Visualización:
# qty_asterisk_directory
ggplot(phishing_bruto, aes(x = qty_asterisk_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (*) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hashtag_directory
ggplot(phishing_bruto, aes(x = qty_hashtag_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (#) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_dollar_directory
ggplot(phishing_bruto, aes(x = qty_dollar_directory)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ($) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
geom_text(aes(label = ..count..), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_percent_directory
ggplot(phishing_bruto, aes(x = qty_percent_directory)) +
geom_density(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (%) en directorio",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_asterisk_directory: En caso de una recategorización, volveríamos a formar
dos niveles, donde recategorizaríamos aquellos con muy pocas observaciones donde
también predominan las webs de phishing en aquel nivel donde más similitud existe
con la objetivo (0): -1, >=0.
qty_hashtag_directory: Tenemos dos niveles diferenciales en cuanto a la
variable objetivo. La dejaríamos como está.
* qty_dollar_directory: Aplicaríamos la misma lógica que en la variable
qty_asterisk_directory: -1, >=0.
* qty_percent_directory: Vemos que 2 valores ya ocupan más del 99% de los datos,
(-1, 0):
phishing_bruto |> filter(qty_percent_directory == -1) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
phishing n porc
1 0 46762 98.427666
2 1 747 1.572334
phishing_bruto |> filter(qty_percent_directory == 0) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
phishing n porc
1 0 11097 27.54965
2 1 29183 72.45035
phishing_bruto |> filter(qty_percent_directory > 0) |>
count(phishing) |> mutate(porc = 100*n/sum(n))
phishing n porc
1 0 141 16.43357
2 1 717 83.56643
Si nos fijamos en las proporciones, cuando no se aplica la característica, casi la totalidad de webs son legítimas nuevamente. Por otro lado, cuando la cantidad es 0 y se aplica la característica, vemos un porcentaje de phishing considerablemente superior a las proporciones globales. Por último, en valores superiores y donde apenas tenemos el 1% de los datos, las proporciones son similares a cuando dicha cantidad es 0, por lo que si decidimos recategorizar, lo más conveniente sería recategorizar aquellos niveles con muy pocos datos con el 0: -1, >=0.
phishing_bruto |> count(directory_length, sort = TRUE) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
directory_length n porc cumul
1 -1 47509 53.593466220 53.59347
2 1 8767 9.889787584 63.48325
3 7 990 1.116789062 64.60004
4 18 958 1.080690830 65.68073
5 6 941 1.061513644 66.74225
6 8 912 1.028799621 67.77105
7 10 893 1.007366296 68.77841
8 19 862 0.972396133 69.75081
9 11 793 0.894559320 70.64537
10 14 789 0.890047041 71.53542
11 17 778 0.877638273 72.41305
12 15 767 0.865229506 73.27828
13 24 761 0.858461087 74.13674
14 20 733 0.826875134 74.96362
15 9 728 0.821234785 75.78485
16 23 726 0.818978646 76.60383
17 16 702 0.791904971 77.39574
18 13 695 0.784008483 78.17975
19 12 674 0.760319018 78.94007
20 22 664 0.749038321 79.68910
21 26 659 0.743397972 80.43250
22 21 634 0.715196228 81.14770
23 5 608 0.685866414 81.83356
24 30 606 0.683610274 82.51717
25 27 603 0.680226065 83.19740
26 28 585 0.659920810 83.85732
27 25 573 0.646383972 84.50371
28 34 567 0.639615554 85.14332
29 4 563 0.635103275 85.77842
30 29 548 0.618182228 86.39661
31 33 534 0.602389252 86.99900
32 31 533 0.601261182 87.60026
33 32 509 0.574187508 88.17444
34 41 431 0.486198066 88.66064
35 39 413 0.465892811 89.12654
36 38 395 0.445587555 89.57212
37 46 393 0.443331416 90.01545
38 40 384 0.433178788 90.44863
39 37 361 0.407233183 90.85587
40 36 334 0.376775300 91.23264
41 35 330 0.372263021 91.60490
42 45 314 0.354213905 91.95912
43 51 292 0.329396370 92.28852
44 44 285 0.321499882 92.61002
45 42 275 0.310219184 92.92023
46 3 268 0.302322696 93.22256
47 43 266 0.300066556 93.52262
48 48 266 0.300066556 93.82269
49 47 241 0.271864812 94.09455
50 55 240 0.270736742 94.36529
51 53 237 0.267352533 94.63264
52 57 236 0.266224463 94.89887
53 58 213 0.240278859 95.13915
54 50 210 0.236894650 95.37604
55 54 200 0.225613952 95.60166
56 52 193 0.217717464 95.81937
57 49 185 0.208692906 96.02807
58 56 174 0.196284138 96.22435
59 59 146 0.164698185 96.38905
60 61 138 0.155673627 96.54472
61 67 136 0.153417487 96.69814
62 60 131 0.147777139 96.84592
63 73 122 0.137624511 96.98354
64 63 106 0.119575395 97.10312
65 62 101 0.113935046 97.21705
66 64 101 0.113935046 97.33099
67 76 98 0.110550836 97.44154
68 65 92 0.103782418 97.54532
69 66 91 0.102654348 97.64797
70 70 84 0.094757860 97.74273
71 81 83 0.093629790 97.83636
72 68 82 0.092501720 97.92886
73 75 82 0.092501720 98.02137
74 69 81 0.091373651 98.11274
75 74 66 0.074452604 98.18719
76 71 62 0.069940325 98.25713
77 80 58 0.065428046 98.32256
78 78 55 0.062043837 98.38460
79 72 53 0.059787697 98.44439
80 89 53 0.059787697 98.50418
81 77 52 0.058659628 98.56284
82 79 48 0.054147348 98.61699
83 82 44 0.049635069 98.66662
84 83 39 0.043994721 98.71062
85 100 37 0.041738581 98.75235
86 91 36 0.040610511 98.79297
87 2 35 0.039482442 98.83245
88 85 35 0.039482442 98.87193
89 86 33 0.037226302 98.90916
90 93 33 0.037226302 98.94638
91 84 32 0.036098232 98.98248
92 90 32 0.036098232 99.01858
93 87 29 0.032714023 99.05129
94 88 28 0.031585953 99.08288
95 108 27 0.030457884 99.11334
96 92 26 0.029329814 99.14267
97 103 26 0.029329814 99.17200
98 169 26 0.029329814 99.20133
99 97 25 0.028201744 99.22953
100 98 24 0.027073674 99.25660
101 110 24 0.027073674 99.28368
102 101 21 0.023689465 99.30737
103 104 21 0.023689465 99.33105
104 94 20 0.022561395 99.35362
105 96 20 0.022561395 99.37618
106 99 20 0.022561395 99.39874
107 111 19 0.021433325 99.42017
108 140 18 0.020305256 99.44048
109 113 17 0.019177186 99.45965
110 95 15 0.016921046 99.47658
111 145 15 0.016921046 99.49350
112 102 14 0.015792977 99.50929
113 109 14 0.015792977 99.52508
114 114 14 0.015792977 99.54088
115 122 14 0.015792977 99.55667
116 159 14 0.015792977 99.57246
117 105 13 0.014664907 99.58713
118 125 13 0.014664907 99.60179
119 118 11 0.012408767 99.61420
120 106 10 0.011280698 99.62548
121 112 10 0.011280698 99.63676
122 129 10 0.011280698 99.64804
123 144 10 0.011280698 99.65932
124 107 9 0.010152628 99.66948
125 115 9 0.010152628 99.67963
126 116 9 0.010152628 99.68978
127 123 9 0.010152628 99.69993
128 128 9 0.010152628 99.71009
129 149 9 0.010152628 99.72024
130 117 8 0.009024558 99.72926
131 120 8 0.009024558 99.73829
132 151 8 0.009024558 99.74731
133 197 8 0.009024558 99.75634
134 119 7 0.007896488 99.76423
135 124 7 0.007896488 99.77213
136 133 7 0.007896488 99.78003
137 135 7 0.007896488 99.78792
138 121 6 0.006768419 99.79469
139 150 6 0.006768419 99.80146
140 126 5 0.005640349 99.80710
141 127 5 0.005640349 99.81274
142 137 5 0.005640349 99.81838
143 143 5 0.005640349 99.82402
144 181 5 0.005640349 99.82966
145 130 4 0.004512279 99.83417
146 131 4 0.004512279 99.83869
147 132 4 0.004512279 99.84320
148 138 4 0.004512279 99.84771
149 139 4 0.004512279 99.85222
150 153 4 0.004512279 99.85674
151 158 4 0.004512279 99.86125
152 166 4 0.004512279 99.86576
153 172 4 0.004512279 99.87027
154 136 3 0.003384209 99.87366
155 154 3 0.003384209 99.87704
156 155 3 0.003384209 99.88042
157 156 3 0.003384209 99.88381
158 173 3 0.003384209 99.88719
159 212 3 0.003384209 99.89058
160 217 3 0.003384209 99.89396
161 270 3 0.003384209 99.89735
162 148 2 0.002256140 99.89960
163 152 2 0.002256140 99.90186
164 161 2 0.002256140 99.90411
165 184 2 0.002256140 99.90637
166 185 2 0.002256140 99.90863
167 187 2 0.002256140 99.91088
168 188 2 0.002256140 99.91314
169 194 2 0.002256140 99.91539
170 195 2 0.002256140 99.91765
171 211 2 0.002256140 99.91991
172 242 2 0.002256140 99.92216
173 391 2 0.002256140 99.92442
174 418 2 0.002256140 99.92668
175 141 1 0.001128070 99.92780
176 147 1 0.001128070 99.92893
177 157 1 0.001128070 99.93006
178 162 1 0.001128070 99.93119
179 163 1 0.001128070 99.93232
180 168 1 0.001128070 99.93344
181 170 1 0.001128070 99.93457
182 175 1 0.001128070 99.93570
183 177 1 0.001128070 99.93683
184 179 1 0.001128070 99.93796
185 183 1 0.001128070 99.93908
186 193 1 0.001128070 99.94021
187 198 1 0.001128070 99.94134
188 204 1 0.001128070 99.94247
189 205 1 0.001128070 99.94360
190 213 1 0.001128070 99.94472
191 215 1 0.001128070 99.94585
192 216 1 0.001128070 99.94698
193 218 1 0.001128070 99.94811
194 219 1 0.001128070 99.94924
195 229 1 0.001128070 99.95036
196 235 1 0.001128070 99.95149
197 236 1 0.001128070 99.95262
198 248 1 0.001128070 99.95375
199 249 1 0.001128070 99.95488
200 251 1 0.001128070 99.95601
201 266 1 0.001128070 99.95713
202 271 1 0.001128070 99.95826
203 276 1 0.001128070 99.95939
204 277 1 0.001128070 99.96052
205 278 1 0.001128070 99.96165
206 301 1 0.001128070 99.96277
207 305 1 0.001128070 99.96390
208 309 1 0.001128070 99.96503
209 316 1 0.001128070 99.96616
210 319 1 0.001128070 99.96729
211 333 1 0.001128070 99.96841
212 346 1 0.001128070 99.96954
213 356 1 0.001128070 99.97067
214 357 1 0.001128070 99.97180
215 375 1 0.001128070 99.97293
216 393 1 0.001128070 99.97405
217 415 1 0.001128070 99.97518
218 450 1 0.001128070 99.97631
219 459 1 0.001128070 99.97744
220 474 1 0.001128070 99.97857
221 478 1 0.001128070 99.97969
222 513 1 0.001128070 99.98082
223 516 1 0.001128070 99.98195
224 520 1 0.001128070 99.98308
225 530 1 0.001128070 99.98421
226 539 1 0.001128070 99.98534
227 543 1 0.001128070 99.98646
228 573 1 0.001128070 99.98759
229 585 1 0.001128070 99.98872
230 592 1 0.001128070 99.98985
231 621 1 0.001128070 99.99098
232 680 1 0.001128070 99.99210
233 700 1 0.001128070 99.99323
234 786 1 0.001128070 99.99436
235 843 1 0.001128070 99.99549
236 915 1 0.001128070 99.99662
237 1083 1 0.001128070 99.99774
238 1271 1 0.001128070 99.99887
239 1286 1 0.001128070 100.00000
# A tibble: 373 × 4
# Groups: directory_length [239]
directory_length phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 1 0 5648 64.4
4 1 1 3119 35.6
5 2 0 3 8.57
6 2 1 32 91.4
7 3 0 81 30.2
8 3 1 187 69.8
9 4 0 173 30.7
10 4 1 390 69.3
# … with 363 more rows
Podemos ver que de los 239 valores únicos que presenta la variable, únicamente dos de ellos ya concentran más del 63% de los datos.
ggplot(phishing_bruto, aes(x = directory_length)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por longitud de directorio",
x = "Longitud de directorio", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Visualizando la variable en un gráfico de densidad, vemos que a partir del 150, prácticamente no se diferencia ninguna observación: se trata de una variable con una alta concentración a la izquierda (cola pesada a la derecha)
phishing n porc
1 0 5590 17.26854
2 1 26781 82.73146
Por otro lado, cuando es mayor que 1, vemos que la probabilidad que la web sea de phishing, es superior al 80%. Teniendo en cuenta que cuando es 1, esta proporción es del 35% y cuando no se aplica la característica (-1) es de apenas un 2%. Podríamos convertir esta variable a factor con 3 niveles: -1, 1, >1.
qty_dot_file count (.) in file qty_hyphen_file count (-) in file qty_underline_file count (_) in file qty_slash_file count (/) in file qty_questionmark_file count (?) in file qty_equal_file count (=) in file qty_at_file count (@) in file qty_and_file count (&) in file qty_exclamation_file count (!) in file qty_space_file count ( ) in file qty_percent_file count (%) in file file_length file length qty_tilde_file count (~) in file qty_comma_file count (,) in file qty_plus_file count (+) in file qty_asterisk_file count (*) in file qty_hashtag_file count (#) in file qty_dollar_file count ($) in file
Comprobamos el número de valores únicos en estas variables:
phishing_bruto |> select(contains("file")) |>
summarise_all(list(~n_distinct(.)))
qty_dot_file qty_hyphen_file qty_underline_file qty_slash_file
1 10 22 18 2
qty_questionmark_file qty_equal_file qty_at_file qty_and_file
1 2 5 4 5
qty_exclamation_file qty_space_file qty_tilde_file qty_comma_file
1 5 9 6 6
qty_plus_file qty_asterisk_file qty_hashtag_file qty_dollar_file
1 10 7 2 2
qty_percent_file file_length
1 37 168
Convertimos a factor:
phishing_bruto <-
phishing_bruto |> mutate(qty_dot_file = as_factor(qty_dot_file),
qty_hyphen_file = as_factor(qty_hyphen_file),
qty_underline_file = as_factor(qty_underline_file),
qty_slash_file = as_factor(qty_slash_file),
qty_questionmark_file = as_factor(qty_questionmark_file),
qty_equal_file = as_factor(qty_equal_file),
qty_at_file = as_factor(qty_at_file),
qty_and_file = as_factor(qty_and_file),
qty_exclamation_file = as_factor(qty_exclamation_file),
qty_space_file = as_factor(qty_space_file),
qty_tilde_file = as_factor(qty_tilde_file),
qty_comma_file = as_factor(qty_comma_file),
qty_plus_file = as_factor(qty_plus_file),
qty_asterisk_file = as_factor(qty_asterisk_file),
qty_hashtag_file = as_factor(qty_hashtag_file),
qty_dollar_file = as_factor(qty_dollar_file))
En este caso no convertimos las variables qty_percent_file ni file_length.
Análisis cualitativo:
# qty_at_file
phishing_bruto |> group_by(qty_at_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 6 × 4
# Groups: qty_at_file [4]
qty_at_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29875 72.7
5 1 1 24 100
6 2 1 1 100
# qty_and_file
phishing_bruto |> group_by(qty_and_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 9 × 4
# Groups: qty_and_file [5]
qty_and_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11236 27.4
4 0 1 29780 72.6
5 1 0 1 0.840
6 1 1 118 99.2
7 2 0 1 50
8 2 1 1 50
9 3 1 1 100
# qty_exclamation_file
phishing_bruto |> group_by(qty_exclamation_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 7 × 4
# Groups: qty_exclamation_file [5]
qty_exclamation_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.4
4 0 1 29812 72.6
5 1 1 86 100
6 2 1 1 100
7 4 1 1 100
# qty_space_file
phishing_bruto |> group_by(qty_space_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 11 × 4
# Groups: qty_space_file [9]
qty_space_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29887 72.7
5 1 1 7 100
6 2 1 1 100
7 3 1 1 100
8 4 1 1 100
9 6 1 1 100
10 7 1 1 100
11 9 1 1 100
# qty_tilde_file
phishing_bruto |> group_by(qty_tilde_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 9 × 4
# Groups: qty_tilde_file [6]
qty_tilde_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11235 27.3
4 0 1 29888 72.7
5 1 0 1 14.3
6 1 1 6 85.7
7 2 1 6 100
8 3 0 1 100
9 4 0 1 100
# qty_comma_file
phishing_bruto |> group_by(qty_comma_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 10 × 4
# Groups: qty_comma_file [6]
qty_comma_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11224 27.3
4 0 1 29891 72.7
5 1 0 4 40
6 1 1 6 60
7 2 1 1 100
8 3 0 10 90.9
9 3 1 1 9.09
10 5 1 1 100
Visualización:
# qty_at_file
ggplot(phishing_bruto, aes(x = qty_at_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (@) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_and_file
ggplot(phishing_bruto, aes(x = qty_and_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (&) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_exclamation_file
ggplot(phishing_bruto, aes(x = qty_exclamation_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (!) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_space_file
ggplot(phishing_bruto, aes(x = qty_space_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ( ) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_tilde_file
ggplot(phishing_bruto, aes(x = qty_tilde_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (~) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_comma_file
ggplot(phishing_bruto, aes(x = qty_comma_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (,) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_at_file: Tenemos dos niveles que recogen practicamente el 100% de los
registros. Cuando no se aplica la característica (-1), casi el 100% de las webs
son legítimas, mientras que cuando es 0, el phishing está presente en más del
70% de los registros. En los otros niveles apenas tenemos datos y se trata de 25
observaciones donde todas resultan ser webs de phishing. De esta manera, podríamos
recategorizar estos dos últimos niveles con el 0: -1, >=0.qty_and_file: Sucede lo mismo que en la anterior variable: -1, >=0.qty_exclamation_file: Nuevamente: -1, >=0.qty_space_file: Una vez más vemos los mismos patrones: -1, >=0.qty_tilde_file: -1, >=0.qty_comma_file: -1, >=0.Análisis cuantitativo:
# qty_dot_file
phishing_bruto |> group_by(qty_dot_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 15 × 4
# Groups: qty_dot_file [10]
qty_dot_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 9594 35.6
4 0 1 17354 64.4
5 1 0 1613 11.7
6 1 1 12126 88.3
7 2 0 28 9.06
8 2 1 281 90.9
9 3 0 3 2.48
10 3 1 118 97.5
11 4 1 15 100
12 5 1 3 100
13 7 1 1 100
14 10 1 1 100
15 12 1 1 100
# qty_hyphen_file
phishing_bruto |> count(qty_hyphen_file) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_hyphen_file n porc cumul
1 -1 47509 53.593466220 53.59347
2 0 39384 44.427899421 98.02137
3 1 860 0.970139993 98.99151
4 2 241 0.271864812 99.26337
5 3 147 0.165826255 99.42920
6 4 129 0.145520999 99.57472
7 5 111 0.125215743 99.69993
8 6 73 0.082349092 99.78228
9 7 42 0.047378930 99.82966
10 8 52 0.058659628 99.88832
11 9 28 0.031585953 99.91991
12 10 17 0.019177186 99.93908
13 11 23 0.025945604 99.96503
14 12 7 0.007896488 99.97293
15 13 11 0.012408767 99.98534
16 14 2 0.002256140 99.98759
17 15 3 0.003384209 99.99098
18 16 1 0.001128070 99.99210
19 17 2 0.002256140 99.99436
20 18 1 0.001128070 99.99549
21 20 1 0.001128070 99.99662
22 21 3 0.003384209 100.00000
# A tibble: 37 × 4
# Groups: qty_hyphen_file [22]
qty_hyphen_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 10663 27.1
4 0 1 28721 72.9
5 1 0 175 20.3
6 1 1 685 79.7
7 2 0 82 34.0
8 2 1 159 66.0
9 3 0 60 40.8
10 3 1 87 59.2
# … with 27 more rows
# qty_underline_file
phishing_bruto |> group_by(qty_underline_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 31 × 4
# Groups: qty_underline_file [18]
qty_underline_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 10796 27.1
4 0 1 29112 72.9
5 1 0 227 29.9
6 1 1 531 70.1
7 2 0 98 32.9
8 2 1 200 67.1
9 3 0 62 77.5
10 3 1 18 22.5
# … with 21 more rows
# qty_slash_file
phishing_bruto |> group_by(qty_slash_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_slash_file [2]
qty_slash_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
# qty_questionmark_file
phishing_bruto |> group_by(qty_questionmark_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_questionmark_file [2]
qty_questionmark_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
# qty_equal_file
phishing_bruto |> group_by(qty_equal_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 8 × 4
# Groups: qty_equal_file [5]
qty_equal_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11237 27.4
4 0 1 29789 72.6
5 1 1 82 100
6 2 1 26 100
7 3 0 1 25
8 3 1 3 75
Visualización:
# qty_dot_file
ggplot(phishing_bruto, aes(x = qty_dot_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (.) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hyphen_file
ggplot(phishing_bruto, aes(x = qty_hyphen_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (-) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_underline_file
ggplot(phishing_bruto, aes(x = qty_exclamation_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (_) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_slash_file
ggplot(phishing_bruto, aes(x = qty_slash_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (/) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_questionmark_file
ggplot(phishing_bruto, aes(x = qty_questionmark_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (?) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_equal_file
ggplot(phishing_bruto, aes(x = qty_equal_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (=) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
qty_dot_file: Podríamos recategorizar los niveles con apenas observaciones
y donde las webs siempre resultan ser de phishing con el nivel 1, donde la proporción
de este tipo de webs está cercana al 90%. Por otro lado el 0 convendría
diferenciarlo también dadas sus proporciones considerablemente diferentes: -1, 0, >0.qty_hyphen_file: vemos que dos niveles (-1 y 0) ya ocupan el 98% de los datos.
De este modo, y viendo que en aquellos niveles menos frecuentes el phishing aparece
más que en el nivel que indica que no se aplica, podríamos volver a crear dos
niveles donde se reagrupen estos últimos con el 0: -1, >=0.qty_underline_file: Nuevamente: -1, >=0.qty_slash_file: Ya tenemos dos niveles distintivos en base sus proporciones
de los dos tipos de webs: la dejamos como está.qty_questionmark_file: la dejamos como está.qty_equal_file: Misma lógica que antes: -1, >0.Análisis cuantitativo:
# qty_plus_file
phishing_bruto |> group_by(qty_plus_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 14 × 4
# Groups: qty_plus_file [10]
qty_plus_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11231 27.3
4 0 1 29885 72.7
5 1 0 2 16.7
6 1 1 10 83.3
7 2 0 1 50
8 2 1 1 50
9 3 1 3 100
10 4 1 1 100
11 5 0 1 100
12 6 0 1 100
13 9 0 1 100
14 19 0 1 100
# qty_asterisk_file
phishing_bruto |> group_by(qty_asterisk_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 9 × 4
# Groups: qty_asterisk_file [7]
qty_asterisk_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29888 72.7
5 1 1 8 100
6 2 1 1 100
7 14 1 1 100
8 20 1 1 100
9 60 1 1 100
# qty_hashtag_file
phishing_bruto |> group_by(qty_hashtag_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_hashtag_file [2]
qty_hashtag_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
# qty_dollar_file
phishing_bruto |> group_by(qty_dollar_file) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_dollar_file [2]
qty_dollar_file phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11238 27.3
4 0 1 29900 72.7
# qty_percent_file
phishing_bruto |> count(qty_percent_file) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_percent_file n porc cumul
1 -1 47509 53.593466220 53.59347
2 0 40800 46.025246201 99.61871
3 1 108 0.121831534 99.74054
4 2 59 0.066556116 99.80710
5 3 25 0.028201744 99.83530
6 4 10 0.011280698 99.84658
7 5 32 0.036098232 99.88268
8 6 13 0.014664907 99.89735
9 7 1 0.001128070 99.89847
10 8 11 0.012408767 99.91088
11 10 2 0.002256140 99.91314
12 12 4 0.004512279 99.91765
13 13 1 0.001128070 99.91878
14 14 2 0.002256140 99.92104
15 15 1 0.001128070 99.92216
16 16 12 0.013536837 99.93570
17 18 5 0.005640349 99.94134
18 20 4 0.004512279 99.94585
19 22 5 0.005640349 99.95149
20 24 3 0.003384209 99.95488
21 28 10 0.011280698 99.96616
22 32 5 0.005640349 99.97180
23 34 5 0.005640349 99.97744
24 36 2 0.002256140 99.97969
25 38 3 0.003384209 99.98308
26 40 1 0.001128070 99.98421
27 42 1 0.001128070 99.98534
28 44 2 0.002256140 99.98759
29 48 1 0.001128070 99.98872
30 51 1 0.001128070 99.98985
31 54 1 0.001128070 99.99098
32 62 2 0.002256140 99.99323
33 66 2 0.002256140 99.99549
34 117 1 0.001128070 99.99662
35 127 1 0.001128070 99.99774
36 162 1 0.001128070 99.99887
37 174 1 0.001128070 100.00000
# A tibble: 46 × 4
# Groups: qty_percent_file [37]
qty_percent_file phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 11118 27.2
4 0 1 29682 72.8
5 1 0 13 12.0
6 1 1 95 88.0
7 2 0 10 16.9
8 2 1 49 83.1
9 3 0 4 16
10 3 1 21 84
# … with 36 more rows
Visualización:
# qty_plus_file
ggplot(phishing_bruto, aes(x = qty_plus_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (+) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_asterisk_file
ggplot(phishing_bruto, aes(x = qty_asterisk_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (*) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hashtag_file
ggplot(phishing_bruto, aes(x = qty_hashtag_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (#) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_dollar_file
ggplot(phishing_bruto, aes(x = qty_dollar_file)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ($) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_percent_file
ggplot(phishing_bruto, aes(x = qty_percent_file)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (%) en archivo",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_plus_file: Sigue los patrones de muchas de las anteriores variables
en cuanto a distribución y proporciones de tipos de web: -1, >=0.qty_asterisk_file: -1, >=0.qty_hashtag_file: la dejamos como está.qty_dollar_file: la dejamos como está.qty_percent_file: Vemos tanto a partir del histograma como de las proporciones
acumuladas del total, que únicamente dos valores de esta variable ya cubren más
del 99% de los registros totales. phishing n porc
1 0 120 35.50296
2 1 218 64.49704
Y cuando observamos las proporciones de los tipo de web en los demás valores, el phishing es más frecuente que las webs legítimas. por lo tanto, podríamos generar una variable binaria donde estos valores se reagrupen con el 0, donde las proporciones de phishing son mucho más similares: -1, >=0.
Para finalizar la exploración de las variables de tipo archivo tenemos la longitud del nombre del archivo.
phishing_bruto |> summarise(mean = mean(file_length),
median = median(file_length),
min = min(file_length),
max = max(file_length))
mean median min max
1 2.743793 -1 -1 1232
Observando los estadísticos descriptivos ya podemos ver que se trata de una variable con valores atípicos muy elevados.
file_length n porc cumul
1 -1 47509 53.593466220 53.59347
2 0 19624 22.137240967 75.73071
3 1 148 0.166954324 75.89766
4 2 570 0.642999763 76.54066
5 3 453 0.511015601 77.05168
6 4 845 0.953218947 78.00490
7 5 1006 1.134838178 79.13973
8 6 1324 1.493564362 80.63330
9 7 1054 1.188985527 81.82228
10 8 1213 1.368348619 83.19063
11 9 4797 5.411350638 88.60198
12 10 2125 2.397148240 90.99913
13 11 1083 1.221699550 92.22083
14 12 916 1.033311900 93.25414
15 13 706 0.796417250 94.05056
16 14 589 0.664433089 94.71499
17 15 555 0.626078717 95.34107
18 16 504 0.568547159 95.90962
19 17 390 0.439947206 96.34957
20 18 271 0.305706905 96.65527
21 19 173 0.195156068 96.85043
22 20 149 0.168082394 97.01851
23 21 322 0.363238463 97.38175
24 22 135 0.152289418 97.53404
25 23 132 0.148905208 97.68294
26 24 106 0.119575395 97.80252
27 25 69 0.077836813 97.88036
28 26 67 0.075580674 97.95594
29 27 64 0.072196465 98.02813
30 28 88 0.099270139 98.12740
31 29 125 0.141008720 98.26841
32 30 57 0.064299976 98.33271
33 31 48 0.054147348 98.38686
34 32 431 0.486198066 98.87306
35 33 29 0.032714023 98.90577
36 34 31 0.034970163 98.94074
37 35 31 0.034970163 98.97571
38 36 42 0.047378930 99.02309
39 37 20 0.022561395 99.04565
40 38 29 0.032714023 99.07837
41 39 24 0.027073674 99.10544
42 40 31 0.034970163 99.14041
43 41 35 0.039482442 99.17989
44 42 50 0.056403488 99.23630
45 43 71 0.080092953 99.31639
46 44 18 0.020305256 99.33669
47 45 19 0.021433325 99.35813
48 46 55 0.062043837 99.42017
49 47 16 0.018049116 99.43822
50 48 35 0.039482442 99.47770
51 49 11 0.012408767 99.49011
52 50 15 0.016921046 99.50703
53 51 14 0.015792977 99.52283
54 52 15 0.016921046 99.53975
55 53 10 0.011280698 99.55103
56 54 10 0.011280698 99.56231
57 55 17 0.019177186 99.58149
58 56 8 0.009024558 99.59051
59 57 7 0.007896488 99.59841
60 58 15 0.016921046 99.61533
61 59 9 0.010152628 99.62548
62 60 10 0.011280698 99.63676
63 61 12 0.013536837 99.65030
64 62 8 0.009024558 99.65932
65 63 7 0.007896488 99.66722
66 64 5 0.005640349 99.67286
67 65 5 0.005640349 99.67850
68 66 6 0.006768419 99.68527
69 67 10 0.011280698 99.69655
70 68 7 0.007896488 99.70445
71 69 4 0.004512279 99.70896
72 70 8 0.009024558 99.71798
73 71 5 0.005640349 99.72362
74 72 6 0.006768419 99.73039
75 73 7 0.007896488 99.73829
76 74 15 0.016921046 99.75521
77 75 7 0.007896488 99.76311
78 76 13 0.014664907 99.77777
79 77 6 0.006768419 99.78454
80 78 9 0.010152628 99.79469
81 79 3 0.003384209 99.79808
82 80 2 0.002256140 99.80033
83 81 2 0.002256140 99.80259
84 82 2 0.002256140 99.80484
85 83 3 0.003384209 99.80823
86 84 2 0.002256140 99.81048
87 85 10 0.011280698 99.82176
88 86 5 0.005640349 99.82741
89 87 3 0.003384209 99.83079
90 88 3 0.003384209 99.83417
91 89 4 0.004512279 99.83869
92 90 6 0.006768419 99.84545
93 91 1 0.001128070 99.84658
94 92 6 0.006768419 99.85335
95 93 1 0.001128070 99.85448
96 94 2 0.002256140 99.85674
97 95 2 0.002256140 99.85899
98 96 3 0.003384209 99.86238
99 97 4 0.004512279 99.86689
100 98 4 0.004512279 99.87140
101 99 2 0.002256140 99.87366
102 100 8 0.009024558 99.88268
103 101 3 0.003384209 99.88606
104 102 1 0.001128070 99.88719
105 103 3 0.003384209 99.89058
106 104 4 0.004512279 99.89509
107 105 2 0.002256140 99.89735
108 108 1 0.001128070 99.89847
109 110 1 0.001128070 99.89960
110 112 1 0.001128070 99.90073
111 113 2 0.002256140 99.90299
112 115 1 0.001128070 99.90411
113 116 1 0.001128070 99.90524
114 117 2 0.002256140 99.90750
115 118 1 0.001128070 99.90863
116 119 3 0.003384209 99.91201
117 120 2 0.002256140 99.91427
118 122 1 0.001128070 99.91539
119 123 2 0.002256140 99.91765
120 124 1 0.001128070 99.91878
121 125 1 0.001128070 99.91991
122 128 1 0.001128070 99.92104
123 129 4 0.004512279 99.92555
124 134 1 0.001128070 99.92668
125 137 2 0.002256140 99.92893
126 138 1 0.001128070 99.93006
127 140 1 0.001128070 99.93119
128 143 1 0.001128070 99.93232
129 144 2 0.002256140 99.93457
130 146 1 0.001128070 99.93570
131 147 1 0.001128070 99.93683
132 153 1 0.001128070 99.93796
133 155 1 0.001128070 99.93908
134 158 10 0.011280698 99.95036
135 168 3 0.003384209 99.95375
136 178 1 0.001128070 99.95488
137 186 3 0.003384209 99.95826
138 189 2 0.002256140 99.96052
139 206 2 0.002256140 99.96277
140 212 1 0.001128070 99.96390
141 231 3 0.003384209 99.96729
142 246 1 0.001128070 99.96841
143 262 1 0.001128070 99.96954
144 282 1 0.001128070 99.97067
145 302 1 0.001128070 99.97180
146 351 1 0.001128070 99.97293
147 352 2 0.002256140 99.97518
148 376 1 0.001128070 99.97631
149 390 1 0.001128070 99.97744
150 405 2 0.002256140 99.97969
151 455 1 0.001128070 99.98082
152 456 1 0.001128070 99.98195
153 473 1 0.001128070 99.98308
154 475 1 0.001128070 99.98421
155 500 1 0.001128070 99.98534
156 504 1 0.001128070 99.98646
157 506 1 0.001128070 99.98759
158 507 1 0.001128070 99.98872
159 524 1 0.001128070 99.98985
160 527 1 0.001128070 99.99098
161 556 1 0.001128070 99.99210
162 615 1 0.001128070 99.99323
163 695 1 0.001128070 99.99436
164 782 1 0.001128070 99.99549
165 840 1 0.001128070 99.99662
166 912 1 0.001128070 99.99774
167 1044 1 0.001128070 99.99887
168 1232 1 0.001128070 100.00000
# A tibble: 264 × 4
# Groups: file_length [168]
file_length phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 46762 98.4
2 -1 1 747 1.57
3 0 0 7335 37.4
4 0 1 12289 62.6
5 1 0 62 41.9
6 1 1 86 58.1
7 2 0 346 60.7
8 2 1 224 39.3
9 3 0 116 25.6
10 3 1 337 74.4
# … with 254 more rows
Vemos que de los 168 valores únicos que hay, únicamente 2 de ellos ya conforman más del 75% del total de registros.
ggplot(phishing_bruto, aes(x = file_length)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por longitud de archivo",
x = "Longitud de archivo", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Como podíamos intuír tras observar los datos, apenas podemos distinguir registros superiores a 0.
phishing n porc
1 0 3903 18.14168
2 1 17611 81.85832
Podemos ver que en el 25% de los registros restantes, la proporción de phishing es considerablemente superior que en el nivel 0. Podríamos convertir esta variable en un factor con 3 niveles: -1, 0, >0.
qty_dot_params count (.) in parameters qty_hyphen_params count (-) in parameters qty_underline_params count (_) in parameters qty_slash_params count (/) in parameters qty_questionmark_params count (?) in parameters qty_equal_params count (=) in parameters qty_at_params count (@) in parameters qty_and_params count (&) in parameters qty_exclamation_params count (!) in parameters qty_space_params count ( ) in parameters qty_tilde_params count (~) in parameters qty_comma_params count (,) in parameters qty_plus_params count (+) in parameters qty_asterisk_params count (*) in parameters qty_hashtag_params count (#) in parameters qty_dollar_params count ($) in parameters qty_percent_params count (%) in parameters params_length parameters length tld_present_params TLD presence in arguments qty_params number of parameters
Comprobamos el número de valores únicos en estas variables:
phishing_bruto |> select(contains("params")) |>
summarise_all(list(~n_distinct(.)))
qty_dot_params qty_hyphen_params qty_underline_params
1 18 29 20
qty_slash_params qty_questionmark_params qty_equal_params
1 17 7 21
qty_at_params qty_and_params qty_exclamation_params
1 6 19 6
qty_space_params qty_tilde_params qty_comma_params qty_plus_params
1 4 3 8 8
qty_asterisk_params qty_hashtag_params qty_dollar_params
1 4 2 5
qty_percent_params params_length tld_present_params qty_params
1 38 354 3 19
Convertimos a factor:
phishing_bruto <-
phishing_bruto |> mutate(qty_dot_params = as_factor(qty_dot_params),
qty_hyphen_params = as_factor(qty_hyphen_params),
qty_underline_params = as_factor(qty_underline_params),
qty_slash_params = as_factor(qty_slash_params),
qty_questionmark_params = as_factor(qty_questionmark_params),
qty_equal_params = as_factor(qty_equal_params),
qty_at_params = as_factor(qty_at_params),
qty_and_params = as_factor(qty_and_params),
qty_exclamation_params = as_factor(qty_exclamation_params),
qty_space_params = as_factor(qty_space_params),
qty_tilde_params = as_factor(qty_tilde_params),
qty_comma_params = as_factor(qty_comma_params),
qty_plus_params = as_factor(qty_plus_params),
qty_asterisk_params = as_factor(qty_asterisk_params),
qty_hashtag_params = as_factor(qty_hashtag_params),
qty_dollar_params = as_factor(qty_dollar_params),
tld_present_params = as_factor(tld_present_params),
qty_params = as_factor(qty_params))
No convertimos una vez más las variables de qty_percent y legth.
Análisis cualitativo:
# qty_at_params
phishing_bruto |> group_by(qty_at_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 8 × 4
# Groups: qty_at_params [6]
qty_at_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 11.9
4 0 1 5155 88.1
5 1 1 1562 100
6 2 1 9 100
7 8 1 1 100
8 10 1 1 100
# qty_and_params
phishing_bruto |> group_by(qty_and_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 32 × 4
# Groups: qty_and_params [19]
qty_and_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 395 10.2
4 0 1 3459 89.8
5 1 0 116 10.7
6 1 1 967 89.3
7 2 0 121 9.32
8 2 1 1177 90.7
9 3 0 41 16.5
10 3 1 208 83.5
# … with 22 more rows
# qty_exclamation_params
phishing_bruto |> group_by(qty_exclamation_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 8 × 4
# Groups: qty_exclamation_params [6]
qty_exclamation_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.38
4 0 1 6703 90.6
5 1 1 20 100
6 2 1 3 100
7 8 1 1 100
8 10 1 1 100
# qty_space_params
phishing_bruto |> group_by(qty_space_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 6 × 4
# Groups: qty_space_params [4]
qty_space_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.36
4 0 1 6724 90.6
5 1 1 3 100
6 4 1 1 100
# qty_tilde_params
phishing_bruto |> group_by(qty_tilde_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 5 × 4
# Groups: qty_tilde_params [3]
qty_tilde_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.36
4 0 1 6723 90.6
5 1 1 5 100
# qty_comma_params
phishing_bruto |> group_by(qty_comma_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 10 × 4
# Groups: qty_comma_params [8]
qty_comma_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.46
4 0 1 6639 90.5
5 1 1 75 100
6 2 1 2 100
7 3 1 8 100
8 4 1 2 100
9 7 1 1 100
10 11 1 1 100
Visualización:
# qty_at_params
ggplot(phishing_bruto, aes(x = qty_at_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (@) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_and_params
ggplot(phishing_bruto, aes(x = qty_and_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (&) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_exclamation_params
ggplot(phishing_bruto, aes(x = qty_exclamation_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (!) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_space_params
ggplot(phishing_bruto, aes(x = qty_space_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ( ) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_tilde_params
ggplot(phishing_bruto, aes(x = qty_tilde_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (~) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_comma_params
ggplot(phishing_bruto, aes(x = qty_comma_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (,) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Vemos que en todas las variables tenemos la mayoría de datos concentrados en el nivel -1, donde la probabilidad de que la web sea de phishing es del 29% aproximadamente. Por otro lado, los demás niveles tienen muchos menos registros y en todos la probabilidad de phishing es cercana o superior al 90%. Por lo tanto, podríamos aplicar la misma transformación para cada una de ellas:
Conclusiones:
qty_at_params: -1, >=0.qty_and_params: -1, >=0.qty_exclamation_params: -1, >=0.qty_space_params: -1, >=0.qty_tilde_params: -1, >=0.qty_comma_params: -1, >=0.Análisis cuantitativo:
# qty_dot_params
phishing_bruto |> group_by(qty_dot_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 26 × 4
# Groups: qty_dot_params [18]
qty_dot_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 652 15.5
4 0 1 3551 84.5
5 1 0 15 0.974
6 1 1 1525 99.0
7 2 0 7 0.844
8 2 1 822 99.2
9 3 0 11 6.15
10 3 1 168 93.9
# … with 16 more rows
# qty_hyphen_params
phishing_bruto |> count(qty_hyphen_params) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_hyphen_params n porc cumul
1 -1 81225 91.627466243 91.62747
2 0 6054 6.829334326 98.45680
3 1 815 0.919376854 99.37618
4 2 174 0.196284138 99.57246
5 3 87 0.098142069 99.67060
6 4 169 0.190643789 99.86125
7 5 32 0.036098232 99.89735
8 6 20 0.022561395 99.91991
9 7 11 0.012408767 99.93232
10 8 10 0.011280698 99.94360
11 9 2 0.002256140 99.94585
12 10 3 0.003384209 99.94924
13 11 6 0.006768419 99.95601
14 12 12 0.013536837 99.96954
15 13 5 0.005640349 99.97518
16 14 3 0.003384209 99.97857
17 15 1 0.001128070 99.97969
18 16 1 0.001128070 99.98082
19 17 1 0.001128070 99.98195
20 18 3 0.003384209 99.98534
21 19 2 0.002256140 99.98759
22 20 1 0.001128070 99.98872
23 21 1 0.001128070 99.98985
24 25 2 0.002256140 99.99210
25 26 2 0.002256140 99.99436
26 27 2 0.002256140 99.99662
27 31 1 0.001128070 99.99774
28 34 1 0.001128070 99.99887
29 35 1 0.001128070 100.00000
# A tibble: 37 × 4
# Groups: qty_hyphen_params [29]
qty_hyphen_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 653 10.8
4 0 1 5401 89.2
5 1 0 27 3.31
6 1 1 788 96.7
7 2 0 10 5.75
8 2 1 164 94.3
9 3 1 87 100
10 4 0 1 0.592
# … with 27 more rows
# qty_underline_params
phishing_bruto |> group_by(qty_underline_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 25 × 4
# Groups: qty_underline_params [20]
qty_underline_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 598 11.5
4 0 1 4584 88.5
5 1 0 78 5.78
6 1 1 1271 94.2
7 2 0 14 4.39
8 2 1 305 95.6
9 3 0 4 1.55
10 3 1 254 98.4
# … with 15 more rows
# qty_slash_params
phishing_bruto |> group_by(qty_slash_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 23 × 4
# Groups: qty_slash_params [17]
qty_slash_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 666 9.96
4 0 1 6023 90.0
5 1 0 21 8.17
6 1 1 236 91.8
7 2 0 2 2.82
8 2 1 69 97.2
9 3 0 4 3.08
10 3 1 126 96.9
# … with 13 more rows
# qty_questionmark_params
phishing_bruto |> group_by(qty_questionmark_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 10 × 4
# Groups: qty_questionmark_params [7]
qty_questionmark_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 693 10.3
4 0 1 6029 89.7
5 1 0 1 0.149
6 1 1 672 99.9
7 2 1 21 100
8 3 1 3 100
9 7 1 1 100
10 9 1 2 100
# qty_equal_params
phishing_bruto |> group_by(qty_equal_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 35 × 4
# Groups: qty_equal_params [21]
qty_equal_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 9 1.35
4 0 1 656 98.6
5 1 0 389 12.3
6 1 1 2782 87.7
7 2 0 115 9.71
8 2 1 1069 90.3
9 3 0 120 9.24
10 3 1 1179 90.8
# … with 25 more rows
Visualización:
# qty_dot_params
ggplot(phishing_bruto, aes(x = qty_dot_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (.) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hyphen_params
ggplot(phishing_bruto, aes(x = qty_hyphen_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (-) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_underline_params
ggplot(phishing_bruto, aes(x = qty_exclamation_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (_) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_slash_params
ggplot(phishing_bruto, aes(x = qty_slash_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (/) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_questionmark_params
ggplot(phishing_bruto, aes(x = qty_questionmark_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (?) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_equal_params
ggplot(phishing_bruto, aes(x = qty_equal_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (=) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Observando tanto el número de registros por nivel como sus proporciones en función de la objetivo, sucede lo mismo que en el grupo anterior de variables tipo cantidad en parámetros. Podríamos volver a convertirlas en factor con las siguientes normas de agrupamiento:
Conclusiones:
qty_dot_file: -1, >=0.qty_hyphen_file: -1, >=0.qty_underline_file: -1, >=0.qty_slash_file: -1, >=0.qty_questionmark_file: -1, >=0.qty_equal_file: -1, >0.Análisis cuantitativo:
# qty_plus_params
phishing_bruto |> group_by(qty_plus_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 13 × 4
# Groups: qty_plus_params [8]
qty_plus_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 637 8.69
4 0 1 6692 91.3
5 1 0 52 75.4
6 1 1 17 24.6
7 2 0 4 30.8
8 2 1 9 69.2
9 3 1 3 100
10 4 0 1 33.3
11 4 1 2 66.7
12 5 1 1 100
13 6 1 4 100
# qty_asterisk_params
phishing_bruto |> group_by(qty_asterisk_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 6 × 4
# Groups: qty_asterisk_params [4]
qty_asterisk_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.36
4 0 1 6723 90.6
5 1 1 4 100
6 4 1 1 100
# qty_hashtag_params
phishing_bruto |> group_by(qty_hashtag_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 4 × 4
# Groups: qty_hashtag_params [2]
qty_hashtag_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.35
4 0 1 6728 90.6
# qty_dollar_params
phishing_bruto |> group_by(qty_dollar_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 7 × 4
# Groups: qty_dollar_params [5]
qty_dollar_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 694 9.37
4 0 1 6713 90.6
5 1 1 6 100
6 3 1 8 100
7 4 1 1 100
# qty_percent_params
phishing_bruto |> count(qty_percent_params) |>
mutate(porc = 100*n/sum(n), cumul = cumsum(porc))
qty_percent_params n porc cumul
1 -1 81225 91.627466243 91.62747
2 0 6747 7.611086670 99.23855
3 1 144 0.162442045 99.40099
4 2 140 0.157929766 99.55892
5 3 21 0.023689465 99.58261
6 4 59 0.066556116 99.64917
7 5 43 0.048507000 99.69768
8 6 40 0.045122790 99.74280
9 7 13 0.014664907 99.75747
10 8 20 0.022561395 99.78003
11 9 30 0.033842093 99.81387
12 10 22 0.024817535 99.83869
13 11 4 0.004512279 99.84320
14 12 17 0.019177186 99.86238
15 13 5 0.005640349 99.86802
16 14 15 0.016921046 99.88494
17 15 8 0.009024558 99.89396
18 16 11 0.012408767 99.90637
19 17 6 0.006768419 99.91314
20 18 16 0.018049116 99.93119
21 19 6 0.006768419 99.93796
22 20 12 0.013536837 99.95149
23 21 6 0.006768419 99.95826
24 22 8 0.009024558 99.96729
25 23 2 0.002256140 99.96954
26 26 6 0.006768419 99.97631
27 27 2 0.002256140 99.97857
28 28 3 0.003384209 99.98195
29 29 1 0.001128070 99.98308
30 30 2 0.002256140 99.98534
31 32 3 0.003384209 99.98872
32 35 1 0.001128070 99.98985
33 36 1 0.001128070 99.99098
34 39 1 0.001128070 99.99210
35 51 4 0.004512279 99.99662
36 52 1 0.001128070 99.99774
37 62 1 0.001128070 99.99887
38 65 1 0.001128070 100.00000
# A tibble: 61 × 4
# Groups: qty_percent_params [38]
qty_percent_params phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 548 8.12
4 0 1 6199 91.9
5 1 0 11 7.64
6 1 1 133 92.4
7 2 0 13 9.29
8 2 1 127 90.7
9 3 0 1 4.76
10 3 1 20 95.2
# … with 51 more rows
# qty_params
phishing_bruto |> group_by(qty_params) |> count(phishing) |>
mutate(porc = 100*n/sum(n))
# A tibble: 32 × 4
# Groups: qty_params [19]
qty_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 11 1.22
4 0 1 891 98.8
5 1 0 388 11.8
6 1 1 2913 88.2
7 2 0 178 15.4
8 2 1 976 84.6
9 3 0 77 5.90
10 3 1 1228 94.1
# … with 22 more rows
Visualización:
# qty_plus_params
ggplot(phishing_bruto, aes(x = qty_plus_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (+) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_asterisk_params
ggplot(phishing_bruto, aes(x = qty_asterisk_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (*) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_hashtag_params
ggplot(phishing_bruto, aes(x = qty_hashtag_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (#) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_dollar_params
ggplot(phishing_bruto, aes(x = qty_dollar_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de ($) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_percent_params
ggplot(phishing_bruto, aes(x = qty_percent_params)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de (%) en parámetros",
x = "cantidad de (.)", y = "Recuento", fill = "Phishing") +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
# qty_params
ggplot(phishing_bruto, aes(x = qty_params)) +
geom_bar(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por cantidad de parámetros",
x = "cantidad de parámetros", y = "Recuento", fill = "Phishing") +
geom_text(aes(label = after_stat(count)), stat = "count", vjust = -1, hjust = 0.5) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Conclusiones:
qty_plus_params: Vemos que hay más frecuencia de webs legítimas en los niveles
con muy pocos registros. Lo mejor en este caso sería realizar el agrupamiento de estos
con el nivel -1, ya que presenta mayor proporción de este tipo de webs y cuenta
con un mayor número de registros (lo que no alteraría en gran medida sus
proporciones con la objetivo): -1, 0 -> agrupamiento en -1.qty_asterisk_params: Volvemos a realizar el agrupamiento junto con el segundo
nivel más frecuente, donde el phishing es mucho más probable: -1, >=0.qty_hashtag_params: La dejamos como está.qty_dollar_params: Una vez más vemos en los niveles con pocos registros que
están solo las webs de phishing presentes: -1, >=0.qty_percent_params: el 99% de los registros se encuentran en el -1 y el 0,
donde el primero contiene el 90% de los datos. phishing n porc
1 0 146 21.62963
2 1 529 78.37037
Vemos que casi el 80% de las webs cuando la cantidad de % en los parámetros es mayor que 0, son de phishing. También, antes habíamos podido ver que cuando la característica no se aplica, volvemos a tener un 29% de este tipo de webs, y cuando la cantidad sí se aplica pero es 0, el phishing asciende al 91%. Por lo tanto, podríamos convertir esta variable en un factor con dos niveles: cuando no se aplica la característica y cuando sí lo hace: -1, >=0.
qty_params: Una vez más, cuando la característica se aplica, la probabilidad de
phishing está cercana o es superior al 90%. Mientras que, cuando no se aplica esta es
inferior a las globales (29%). Podríamos volver a convertir a factor y agrupar niveles:
-1, >=0.Variable que indica la longitud de caracteres en el parámetro.
params_length n porc cumul
1 -1 81225 91.627466243 81225
2 1 22 0.024817535 81247
3 2 29 0.032714023 81276
4 3 94 0.106038557 81370
5 4 73 0.082349092 81443
6 5 79 0.089117511 81522
7 6 139 0.156801697 81661
8 7 89 0.100398209 81750
9 8 125 0.141008720 81875
10 9 139 0.156801697 82014
11 10 83 0.093629790 82097
12 11 106 0.119575395 82203
13 12 104 0.117319255 82307
14 13 137 0.154545557 82444
15 14 83 0.093629790 82527
16 15 66 0.074452604 82593
17 16 164 0.185003441 82757
18 17 77 0.086861372 82834
19 18 88 0.099270139 82922
20 19 107 0.120703464 83029
21 20 117 0.131984162 83146
22 21 148 0.166954324 83294
23 22 157 0.177106952 83451
24 23 212 0.239150789 83663
25 24 121 0.136496441 83784
26 25 103 0.116191185 83887
27 26 115 0.129728022 84002
28 27 175 0.197412208 84177
29 28 84 0.094757860 84261
30 29 100 0.112806976 84361
31 30 84 0.094757860 84445
32 31 96 0.108294697 84541
33 32 81 0.091373651 84622
34 33 86 0.097013999 84708
35 34 25 0.028201744 84733
36 35 38 0.042866651 84771
37 36 79 0.089117511 84850
38 37 28 0.031585953 84878
39 38 54 0.060915767 84932
40 39 37 0.041738581 84969
41 40 69 0.077836813 85038
42 41 40 0.045122790 85078
43 42 163 0.183875371 85241
44 43 21 0.023689465 85262
45 44 32 0.036098232 85294
46 45 20 0.022561395 85314
47 46 40 0.045122790 85354
48 47 29 0.032714023 85383
49 48 41 0.046250860 85424
50 49 22 0.024817535 85446
51 50 24 0.027073674 85470
52 51 36 0.040610511 85506
53 52 32 0.036098232 85538
54 53 27 0.030457884 85565
55 54 15 0.016921046 85580
56 55 14 0.015792977 85594
57 56 19 0.021433325 85613
58 57 19 0.021433325 85632
59 58 24 0.027073674 85656
60 59 51 0.057531558 85707
61 60 19 0.021433325 85726
62 61 19 0.021433325 85745
63 62 34 0.038354372 85779
64 63 24 0.027073674 85803
65 64 54 0.060915767 85857
66 65 28 0.031585953 85885
67 66 16 0.018049116 85901
68 67 32 0.036098232 85933
69 68 24 0.027073674 85957
70 69 21 0.023689465 85978
71 70 28 0.031585953 86006
72 71 24 0.027073674 86030
73 72 27 0.030457884 86057
74 73 58 0.065428046 86115
75 74 50 0.056403488 86165
76 75 49 0.055275418 86214
77 76 16 0.018049116 86230
78 77 17 0.019177186 86247
79 78 15 0.016921046 86262
80 79 22 0.024817535 86284
81 80 5 0.005640349 86289
82 81 18 0.020305256 86307
83 82 26 0.029329814 86333
84 83 35 0.039482442 86368
85 84 53 0.059787697 86421
86 85 14 0.015792977 86435
87 86 11 0.012408767 86446
88 87 68 0.076708744 86514
89 88 6 0.006768419 86520
90 89 14 0.015792977 86534
91 90 29 0.032714023 86563
92 91 5 0.005640349 86568
93 92 17 0.019177186 86585
94 93 15 0.016921046 86600
95 94 9 0.010152628 86609
96 95 13 0.014664907 86622
97 96 10 0.011280698 86632
98 97 7 0.007896488 86639
99 98 12 0.013536837 86651
100 99 13 0.014664907 86664
101 100 20 0.022561395 86684
102 101 8 0.009024558 86692
103 102 8 0.009024558 86700
104 103 7 0.007896488 86707
105 104 12 0.013536837 86719
106 105 7 0.007896488 86726
107 106 9 0.010152628 86735
108 107 7 0.007896488 86742
109 108 7 0.007896488 86749
110 109 16 0.018049116 86765
111 110 13 0.014664907 86778
112 111 16 0.018049116 86794
113 112 14 0.015792977 86808
114 113 7 0.007896488 86815
115 114 19 0.021433325 86834
116 115 21 0.023689465 86855
117 116 16 0.018049116 86871
118 117 5 0.005640349 86876
119 118 15 0.016921046 86891
120 119 35 0.039482442 86926
121 120 11 0.012408767 86937
122 121 6 0.006768419 86943
123 122 9 0.010152628 86952
124 123 7 0.007896488 86959
125 124 6 0.006768419 86965
126 125 16 0.018049116 86981
127 126 6 0.006768419 86987
128 127 8 0.009024558 86995
129 128 3 0.003384209 86998
130 129 3 0.003384209 87001
131 130 4 0.004512279 87005
132 131 41 0.046250860 87046
133 132 8 0.009024558 87054
134 134 4 0.004512279 87058
135 135 7 0.007896488 87065
136 136 6 0.006768419 87071
137 137 12 0.013536837 87083
138 139 11 0.012408767 87094
139 140 6 0.006768419 87100
140 141 2 0.002256140 87102
141 142 6 0.006768419 87108
142 143 5 0.005640349 87113
143 144 8 0.009024558 87121
144 145 7 0.007896488 87128
145 146 3 0.003384209 87131
146 147 2 0.002256140 87133
147 148 7 0.007896488 87140
148 149 12 0.013536837 87152
149 150 4 0.004512279 87156
150 151 1 0.001128070 87157
151 152 4 0.004512279 87161
152 153 4 0.004512279 87165
153 154 9 0.010152628 87174
154 155 9 0.010152628 87183
155 156 4 0.004512279 87187
156 157 120 0.135368371 87307
157 158 4 0.004512279 87311
158 159 9 0.010152628 87320
159 160 8 0.009024558 87328
160 161 12 0.013536837 87340
161 162 9 0.010152628 87349
162 163 4 0.004512279 87353
163 164 16 0.018049116 87369
164 165 527 0.594492763 87896
165 166 8 0.009024558 87904
166 167 5 0.005640349 87909
167 168 10 0.011280698 87919
168 169 4 0.004512279 87923
169 170 2 0.002256140 87925
170 171 14 0.015792977 87939
171 172 6 0.006768419 87945
172 173 43 0.048507000 87988
173 174 44 0.049635069 88032
174 175 13 0.014664907 88045
175 176 7 0.007896488 88052
176 177 9 0.010152628 88061
177 178 9 0.010152628 88070
178 179 8 0.009024558 88078
179 180 9 0.010152628 88087
180 181 4 0.004512279 88091
181 182 4 0.004512279 88095
182 183 6 0.006768419 88101
183 184 6 0.006768419 88107
184 185 4 0.004512279 88111
185 186 6 0.006768419 88117
186 187 4 0.004512279 88121
187 188 4 0.004512279 88125
188 189 5 0.005640349 88130
189 190 11 0.012408767 88141
190 191 6 0.006768419 88147
191 192 3 0.003384209 88150
192 193 10 0.011280698 88160
193 194 9 0.010152628 88169
194 195 9 0.010152628 88178
195 196 5 0.005640349 88183
196 197 3 0.003384209 88186
197 198 6 0.006768419 88192
198 199 5 0.005640349 88197
199 200 1 0.001128070 88198
200 201 2 0.002256140 88200
201 202 1 0.001128070 88201
202 203 2 0.002256140 88203
203 204 3 0.003384209 88206
204 206 2 0.002256140 88208
205 207 2 0.002256140 88210
206 208 5 0.005640349 88215
207 209 18 0.020305256 88233
208 210 3 0.003384209 88236
209 211 5 0.005640349 88241
210 212 2 0.002256140 88243
211 213 3 0.003384209 88246
212 214 40 0.045122790 88286
213 215 3 0.003384209 88289
214 216 4 0.004512279 88293
215 217 4 0.004512279 88297
216 218 3 0.003384209 88300
217 219 3 0.003384209 88303
218 220 7 0.007896488 88310
219 221 2 0.002256140 88312
220 222 5 0.005640349 88317
221 223 3 0.003384209 88320
222 224 8 0.009024558 88328
223 225 4 0.004512279 88332
224 226 9 0.010152628 88341
225 227 13 0.014664907 88354
226 228 6 0.006768419 88360
227 229 9 0.010152628 88369
228 230 13 0.014664907 88382
229 231 8 0.009024558 88390
230 232 3 0.003384209 88393
231 233 4 0.004512279 88397
232 234 3 0.003384209 88400
233 235 12 0.013536837 88412
234 236 19 0.021433325 88431
235 237 3 0.003384209 88434
236 238 4 0.004512279 88438
237 239 6 0.006768419 88444
238 240 2 0.002256140 88446
239 241 7 0.007896488 88453
240 242 9 0.010152628 88462
241 243 5 0.005640349 88467
242 245 2 0.002256140 88469
243 246 5 0.005640349 88474
244 247 3 0.003384209 88477
245 248 1 0.001128070 88478
246 250 1 0.001128070 88479
247 251 4 0.004512279 88483
248 252 1 0.001128070 88484
249 253 1 0.001128070 88485
250 254 1 0.001128070 88486
251 255 3 0.003384209 88489
252 257 7 0.007896488 88496
253 258 4 0.004512279 88500
254 259 3 0.003384209 88503
255 260 2 0.002256140 88505
256 261 2 0.002256140 88507
257 262 1 0.001128070 88508
258 264 3 0.003384209 88511
259 265 1 0.001128070 88512
260 266 2 0.002256140 88514
261 267 2 0.002256140 88516
262 268 4 0.004512279 88520
263 269 1 0.001128070 88521
264 271 1 0.001128070 88522
265 272 1 0.001128070 88523
266 273 2 0.002256140 88525
267 274 2 0.002256140 88527
268 275 1 0.001128070 88528
269 276 10 0.011280698 88538
270 277 2 0.002256140 88540
271 278 1 0.001128070 88541
272 281 1 0.001128070 88542
273 283 2 0.002256140 88544
274 284 1 0.001128070 88545
275 285 3 0.003384209 88548
276 286 3 0.003384209 88551
277 287 1 0.001128070 88552
278 288 1 0.001128070 88553
279 289 3 0.003384209 88556
280 290 3 0.003384209 88559
281 293 1 0.001128070 88560
282 295 1 0.001128070 88561
283 296 2 0.002256140 88563
284 297 1 0.001128070 88564
285 298 1 0.001128070 88565
286 304 2 0.002256140 88567
287 308 1 0.001128070 88568
288 309 1 0.001128070 88569
289 310 2 0.002256140 88571
290 311 1 0.001128070 88572
291 312 2 0.002256140 88574
292 313 1 0.001128070 88575
293 315 1 0.001128070 88576
294 319 1 0.001128070 88577
295 322 1 0.001128070 88578
296 323 1 0.001128070 88579
297 324 1 0.001128070 88580
298 331 1 0.001128070 88581
299 333 1 0.001128070 88582
300 337 1 0.001128070 88583
301 340 1 0.001128070 88584
302 342 1 0.001128070 88585
303 344 1 0.001128070 88586
304 345 1 0.001128070 88587
305 357 1 0.001128070 88588
306 377 1 0.001128070 88589
307 384 1 0.001128070 88590
308 396 1 0.001128070 88591
309 401 1 0.001128070 88592
310 408 1 0.001128070 88593
311 418 2 0.002256140 88595
312 428 1 0.001128070 88596
313 447 1 0.001128070 88597
314 453 1 0.001128070 88598
315 458 1 0.001128070 88599
316 459 1 0.001128070 88600
317 460 1 0.001128070 88601
318 461 1 0.001128070 88602
319 462 1 0.001128070 88603
320 464 1 0.001128070 88604
321 470 1 0.001128070 88605
322 473 1 0.001128070 88606
323 488 5 0.005640349 88611
324 492 1 0.001128070 88612
325 513 1 0.001128070 88613
326 523 1 0.001128070 88614
327 536 1 0.001128070 88615
328 538 1 0.001128070 88616
329 543 1 0.001128070 88617
330 551 1 0.001128070 88618
331 557 1 0.001128070 88619
332 565 1 0.001128070 88620
333 579 1 0.001128070 88621
334 589 2 0.002256140 88623
335 596 1 0.001128070 88624
336 606 2 0.002256140 88626
337 617 1 0.001128070 88627
338 664 2 0.002256140 88629
339 667 1 0.001128070 88630
340 676 1 0.001128070 88631
341 700 3 0.003384209 88634
342 702 1 0.001128070 88635
343 704 1 0.001128070 88636
344 713 1 0.001128070 88637
345 785 1 0.001128070 88638
346 808 1 0.001128070 88639
347 823 1 0.001128070 88640
348 879 1 0.001128070 88641
349 885 1 0.001128070 88642
350 889 1 0.001128070 88643
351 941 1 0.001128070 88644
352 1191 1 0.001128070 88645
353 1950 1 0.001128070 88646
354 4094 1 0.001128070 88647
# A tibble: 446 × 4
# Groups: params_length [354]
params_length phishing n porc
<int> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 1 1 22 100
4 2 1 29 100
5 3 0 2 2.13
6 3 1 92 97.9
7 4 0 11 15.1
8 4 1 62 84.9
9 5 0 10 12.7
10 5 1 69 87.3
# … with 436 more rows
Vemos que solamente un valor que puede tomar la variable (-1) de los 354 posibles, ya presenta más del 90% de los registros donde volvemos a ver una proporción de webs de phishing del 29%.
phishing n porc
1 0 694 9.350579
2 1 6728 90.649421
Por otro lado vemos que para el resto de valores, cuando la característica se aplica, teniendo o no cantidad de caracteres, la probabilidad de que la web sea de phishing es superior al 90%.
Como era de esperar, tenemos un valor máximo muy elevado.
ggplot(phishing_bruto, aes(x = params_length)) +
geom_histogram(aes(fill = phishing), alpha = 0.7) +
labs(title = "Webs de Phishing por longitud del parámetro",
x = "Longitud de la URL", y = "Recuento", fill = "Phishing") +
scale_y_continuous(breaks = seq(0, 8000000, 15000)) +
scale_x_continuous(breaks = seq(0, 4000, 500)) +
scale_fill_manual(values = c("#6C3483", "#FEDD00")) +
theme_minimal()
Viendo el gráfico, carece de sentido tener unos valores tan altos en la variable, la distribución es muy asimétrica. Teniendo en cuenta la información anterior, podríamos simplificar en gran medida esta variable convirtiéndola a factor y creando dos únicos niveles: -1, >=1.
Variable que indica si el enlace presenta los caracteres tld en los parámetros.
tld_present_params n porc cumul
1 -1 81225 91.627466 91.62747
2 0 5247 5.918982 97.54645
3 1 2175 2.453552 100.00000
# A tibble: 6 × 4
# Groups: tld_present_params [3]
tld_present_params phishing n porc
<fct> <fct> <int> <dbl>
1 -1 0 57306 70.6
2 -1 1 23919 29.4
3 0 0 675 12.9
4 0 1 4572 87.1
5 1 0 19 0.874
6 1 1 2156 99.1
Vemos que uno de los 3 niveles (-1) ya contiene más del 90% de los datos totales. Si nos fijamos en su distribución con la objetivo, los otros dos niveles presentan una proporción de webs de pshishing del 87% y 99% respectivamente. Por otro lado, volvemos a tener un 29% de phishing en el nivel más frecuente. Teniendo en cuenta el número de observaciones por nivel y la distribución de los tipos de web, podríamos simplificar esta variable conviritiendola a factor y diferenciando 2 niveles: -1, >=0.